Enhancements to drone services including UAV-as-a-service and volumetric modelling

Abstract

A computer platform allows a heterogenous supply of remote-controlled unmanned aerial vehicles to be dynamically maintained and used on-demand. A computer platform manages supply of and demand for drones, matches drones to users based on the supply and demand. Geofencing for each drone can be determined and applied to control flight. Alerts can activate drones to be directed to capture live streaming media from a location. A cryptocurrency enables economic incentives to be provided to users of the system. The computer platform can be used to identify and track unique organisms. The platform can use gamification by tracking user performance and comparing tracked performance among users or to specified goals. A click- to-focus operation allows camera and directional control for the drone.

Description

ENHANCEMENTS TO DRONE SERVICESINCLUDING UAV-AS-A-SERVICE AND VOLUMETRIC MODELLINGBACKGROUND

[0001] Quadcopter unmanned aerial vehicles (UAVs), typically drones, often are equipped with high-definition video cameras with the capability to transmit captured digital video in real-time to a remote controller device. A system and method for transmitting a real-time video stream over a wide-area-network (WAN) to a remote end user who can manipulate the UAV controls that include both the movement of the drone as well as the direction and focus on the attached imaging devices has been described in PCT Publication WO2021231584A1, which is hereby incorporated by reference.SUMMARY

[0002] This Summary introduces a selection of concepts in simplified form that are described further below in the Detailed Description. This Summary neither identifies key or essential features, nor limits the scope, of the claimed subject matter.

[0003] Techniques described herein enable a computer platform to connect the growing supply of drones to consumers seeking to experience remote locations for various purposes, such as entertainment, education, truth verification of events, monitoring and surveillance, research, and nature photography. These techniques described herein allow a heterogenous supply of remote and remote controlled unmanned aerial vehicles, i.e., drones to be dynamically built and maintained, and then used on-demand for a variety of dynamically changing remote drone operations by a dynamically changing group of end users using a heterogeneous set of enduser computing devices. A computer platform supports management of the supply of the drones, management of demand for and use of the drones, and matching drones to appropriate uses for the drones based on the supply and demand. The computer platform enables and manages a distributed system of heterogeneous endpoints with various capabilities.

[0004] The computer platform performs several functions to support initiating remote drone operations. The computer platform registers and authenticates untrusted endpoints, i.e., drones, onto the flight platform. The computer platform registers and authenticates untrusted end users and end user computing devices requesting operations. The computer platform matches drones to requested operations.

[0005] The computer platform performs several functions to support executing remote drone operations. The computer platform generates and maintains allowable flight zones for each remote drone operation. The computer platform communicates remote command and control data from end user computer devices, or other system devices, to the drones to execute the operation. The computer platform ensures drone operation remains within the allowable flight zone and other parameters. The computer platform delivers data, such as status information, telemetry data, streaming video, streaming audio, or other data, or combinations of these, from the drone and other system devices to the end user computing devices.

[0006] Techniques described herein also support using a collection of drones to provide a source of truth about an event occurring at a location. In response to an alert associated with a location, one or more drones available near the location can be selected and remotely controlled, whether by an operator or autonomously, to fly to the location. Video, audio, or other sensors, or combinations of these, on the drones can be directed to capture and transmit data from the location. For example, live streaming of audio and video of events occurring at the location can be transmitted by the drone. The cloud service associated with the drones can receive and store the data transmitted by the drone as a source of truth about the event as it happens.

[0007] Techniques described herein also support using streaming media data from multiple perspectives near a location to define a volumetric space near the location. Based on information from the streaming media from different perspectives, media streams can be combined to generate views from a particular point and camera angle within the volumetric space. With multiple drones in the location, a selected viewpoint can enable real-time source of truth evaluation.

[0008] Techniques described herein also support using geolocation and functionality information about a drone and information defining excluded geographical regions to define a geofence or allowable flight space in three dimensions. Geolocation and range information for a drone can define a volume, such as a cylindrical space, in which a drone can operate. One or more of viewshed analysis, maximum altitude restrictions, and other regions of restricted flight space can define the excluded geographical regions. The intersection of the cylindrical space and excluded geographical regions are used to compute the geofence.

[0009] Such technology enables a shared economy commerce platform for enabling a marketplace that connects a supply of available, unmanned aircraft vehicles (UAV) with ademand for UAV services — herein referred to as UAV-as-a-Service, with a variety of services.

[0010] A drone service typically includes a drone with sensors, such as video (optical or infrared or both) and a remote controller unit. The drone and remote controller unit contain radio transmission modules that can generate a line-of-sight radio frequency communication channel. The remote controller unit exposes a physical interface that a pilot-in-command (PIC) can manipulate as a means to control the drone’s navigation and its onboard sensors.

[0011] Early drone services were limited to an application resident on the remote-control unit. This application would receive the drone’s transmitted sensor and telemetry data and display that information using the remote controller’s graphical interface. For example, the drone’s captured video would be processed by the remote controller application and displayed as a virtual “cockpit” on the remote controller unit. The PIC would be able to remotely navigate the drone and receive a real-time video broadcast from the vantage point of the aerial drone. Example uses of early drone services include: a utility company inspecting a power line at a physically inaccessible location; a fire brigade investigating the spread of a wildfire; and a wildlife conservancy counting the size of a migrating herd.

[0012] Current drone services extend this client application model to a client-server or clientcloud model. The drone’s sensor and telemetry data, proxied by the remote controller unit or transmitted directly to a radio-frequency (RF) receiver, are relayed to a server residing on a WAN containing a myriad of addressable client computing devices each with an application to process the drone’s sensor data. This wide-area remote model 1) extends the drone service to a multiplicity of physically remote end-users and 2) decouples the PICs from drones with end-users who can assume navigational control over the WAN.

[0013] Factors aiding the advancement of these recent drone services are changes in both regulation and technology. National civil aviation authorities began permitting beyond- visual-line-of-site (BVLOS) drone flights and remote manipulation of UAV controls (remote co-pilots). With these regulatory changes, the PIC can reside geographically distant from the drone and access the drone through a global network that allows for low latency, high- bandwidth transmission. Solutions are now offered on several globally available networks: 4G and 5G cellular networks, low-earth-orbit (LEO) satellite networks, and fiber-to-the-edge (FTTE) networks.

[0014] Drone services eliminate the geographic barrier for real-time access to remote information. Unlike broadcast media, such as television broadcast, current drone servicesprovide remote individuals personal engagement and impactful agency. With access to the drone’s camera and navigation controls, a remote end-user has full control over the experience. Example scenarios include a classroom taking a virtual field trip to track the seasonal whale migration; a safari tour from a 50 meter above ground vantage point to avoid disturbing the wildlife; a distributed team of researchers evaluating the emergence of an erupting volcano.

[0015] Because of significant market entry costs, ongoing operational expenses, and complex technical challenges, demand for drone services has been limited. The techniques described herein enable a platform, herein called “UAV-as-a-Service,” that supports a digital marketplace, connecting a supply of remote drones with a demand for remote drone operations. Examples of UAVs services enabled by these new methods include virtual tourism, “source of truth” investigation, monitoring and security, photography, and research.

[0016] As an example implementation, a UAV-as-a-Service can be implemented using a computer platform that supports 1) a marketplace of offers from UAV drone operators and service demand from end-users, 2) transaction services such as payment handling, scheduling, ID validation, support, regulatory approvals, liability insurance, and quality controls; 3) cloud platform services for enabling and managing a distributed system of heterogeneous endpoints with various capabilities (drones with sensor; consumers with computing devices; video capture devices, etc.), and 4) UAV-as-a -Service services.Examples of such services include but are not limited to registering and authenticating untrusted endpoints, i.e. drones, onto the flight platform; auto generating geofencing; storing video, audio, or other sensor data as a “source of truth”; and multiplexing near-by drones telemetry and audio / video data for an enhanced experience.

[0017] Systems that support remote drone flights can be developed to support conservation of biodiversity, enhance ecotourism, and provide other features. For example, in some implementations, a cryptocurrency, herein called “naturecoin” tokens, enables economic incentives to be provided to users of the system. In some implementations, techniques are provided to identify and track unique organisms. In some implementations, gamification can be introduced in the system by tracking user performance and comparing tracked performance among users or to specified goals. In some implementations, the system can be used for remote shopping. In some implementations, a “click-to-focus” technique allows a user selection on a point in an image to move the drone or position the camera on the drone in a direction based on the selected point. In some implementations, data channels for audio,video, telemetry, and metadata can be established as distinct channels enabling different transmission protocols to be used for different purposes.

[0018] Accordingly, in one aspect, a computer system supports using remote controlled unmanned aerial vehicles to record streaming media as a source of truth of an event occurring at a location. The system includes a cloud service comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network with a radio for communicating data to and from unmanned aerial vehicles. The cloud service includes an application that configures the cloud service to select one or more unmanned aerial vehicles based on the location in response to an alert about an event at a location. The cloud service remotely controls flight of the selected unmanned aerial vehicles to the location. The cloud service directs the selected unmanned aerial vehicles to capture and transmit live streaming media from the location over the radio network. The cloud service receives and stores the streaming media data from the selected unmanned aerial vehicles in computer storage as a source of truth of events occurring at the location.

[0019] In one aspect, a computer system supports using remote controlled unmanned aerial vehicles to record streaming media of a location to generate a volumetric model of three- dimensional space around the location. The system includes a cloud service comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network with a radio for communicating data to and from unmanned aerial vehicles. The cloud service includes an application that configures the cloud service to remotely control flight of one or more unmanned aerial vehicles to the location. The cloud service directs the unmanned aerial vehicles to capture and transmit live streaming media from the location. The cloud service receives and stores in computer storage streaming media data of a plurality of perspectives of the location from the unmanned aerial vehicles. The cloud service combines the streaming media from the plurality of perspectives from the unmanned aerial vehicles into a three-dimensional volumetric model of space around the location.

[0020] In one aspect, a computer system supports using remote-controlled unmanned aerial vehicles to a location within an allowable flight space. The system includes a cloud service comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network with a radio for communicating data to and from unmanned aerial vehicles. The cloud service includes an application thatconfigures the cloud service to specify a volumetric space centered at the geospatial location with a radius based on the specified range and with a maximum flight altitude, based on a geospatial location of launch of an unmanned aerial vehicle and a specified range for communication between the unmanned aerial vehicle and a remote control for the unmanned aerial vehicle. The cloud service defines the geofence based on an intersection between the volumetric space and an excluded region. The cloud service remotely controls flight of the unmanned aerial vehicle to the location while ensuring the unmanned aerial vehicle remains with the geofence.

[0021] In one aspect, a computer system supports use of a heterogenous supply of remote controlled unmanned aerial vehicles for a remote operation by end users using a heterogeneous set of end user computing devices. The system includes a cloud service comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network with a radio for communicating data to and from unmanned aerial vehicles. The cloud service includes an application that configures the cloud service to receive and store data describing a geospatial location of launch and capabilities of the unmanned aerial vehicle in response to a request to register an unmanned aerial vehicle. The cloud service selects a registered unmanned aerial vehicle for the remote operation based on the location and parameters of the remote operation in response to a request from an end user computing device to perform a remote operation at a location using an unmanned aerial vehicle. The cloud service transmits command and control data from the end user computing device to the unmanned aerial vehicle selected for the remote operation over the radio network. The cloud service transmits status and streaming media data from the unmanned aerial vehicle selected for the remote operation to the end user computing device over the wide-area network.

[0022] In one aspect, a computer system supports use of remote-controlled unmanned aerial vehicles by multiple users. The system includes a cloud service comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network with a radio for communicating data to and from unmanned aerial vehicles. The cloud service includes an application that configures the cloud service to receive an indication of a selected point within an image displayed in a graphical user interface of the user computer from a user computer. The cloud service determines a direction with respect to the drone based on the selected point. The cloud service controls operation of the drone based on the determined direction.

[0023] In one aspect, a computer system supports use of remote-controlled unmanned aerial vehicles by multiple users. The system includes a cloud service comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network with a radio for communicating data to and from unmanned aerial vehicles. The cloud service includes an application that configures the cloud service to track activities performed through the user using one of the unmanned aerial vehicles for each user. The application compares the tracked activities of a first user to tracked activities of a second user or to a threshold for tracked activities to output a comparison result.

[0024] In one aspect, a computer system supports use of remote-controlled unmanned aerial vehicles by multiple users. The system includes a cloud service comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network with a radio for communicating data to and from unmanned aerial vehicles. The system also includes a cryptocurrency system supporting cryptocurrency tokens connected to the wide-area network. The cloud service includes an application that configures cloud service to access the cryptocurrency system. The cloud service monitors interactions of users with the cloud service. The cloud service transfers cryptocurrency tokens to users based on the monitored interactions of the users with the cloud service.

[0025] In one aspect, a computer system supports use of remote controlled unmanned aerial vehicles by multiple users. The system includes a cloud service comprising at least one server computer connected to a wide-area network, at least one network appliance connected to the wide-area network with a radio for communicating UAV data to and from unmanned aerial vehicles, and at least one mobile device appliance connected to the wide-area network with a radio for communicating mobile data to and from mobile devices. The cloud service includes an application that configures cloud service to transition a user between communication with the unmanned aerial vehicles and the mobile devices. The cloud service manages transactions associated with the mobile devices.

[0026] In one aspect, a computer system supports use of remote-controlled unmanned aerial vehicles by multiple users. The system includes a cloud service comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network with a radio for communicating data to and from unmanned aerial vehicles. The cloud service includes an application that configures cloudservice to process the streaming media data from the unmanned aerial vehicles to identify organisms. The cloud service stores data about identified organisms in a database.

[0027] In one aspect, a computer system dynamically prices insurance for unmanned aerial vehicle (UAV) operations. The system includes a cloud service comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network with a radio for communicating data to and from unmanned aerial vehicles. The cloud service includes an application that configures cloud service to receive a request for insurance coverage for a UAV operation at a specified location from an end user computing device. The cloud service obtains data describing multiple risk factors associated with the specified location in real-time. The cloud service analyzes the obtained real-time risk factor data to generate a risk profile specific to the requested UAV operation and, based on the generated risk profile, an insurance premium for the requested UAV operation.

[0028] In one aspect, a computer-implemented process supports use of remote controlled unmanned aerial vehicles to record streaming media as a source of truth of an event occurring at a location. The process involves selecting one or more unmanned aerial vehicles based on the location in response to an alert about an event at a location. The process remotely controls flight of the selected unmanned aerial vehicles to the location. The process directs the selected unmanned aerial vehicles to capture and transmit live streaming media from the location over the radio network. The process receives and stores the streaming media data from the selected unmanned aerial vehicles in computer storage as a source of truth of events occurring at the location.

[0029] In one aspect, a computer-implemented process supports use of remote controlled unmanned aerial vehicles to record streaming media of a location to generate a volumetric model of three-dimensional space around the location. The process involves remotely controlling flight of one or more unmanned aerial vehicles to the location. The process directs the unmanned aerial vehicles to capture and transmit live streaming media from the location. The process receives and stores in computer storage streaming media data of a plurality of perspectives of the location from the unmanned aerial vehicles. The process combines the streaming media from the plurality of perspectives from the unmanned aerial vehicles into a three-dimensional volumetric model of space around the location.

[0030] In one aspect, a computer-implemented process supports use of remote-controlled unmanned aerial vehicles to a location within an allowable flight space. The process involves specifying a volume centered at the geospatial location and a shape based on the specifiedrange and with a maximum flight altitude, based on a geospatial location of launch of an unmanned aerial vehicle and a specified range for communication between the unmanned aerial vehicle and a remote control for the unmanned aerial vehicle. The process defines the geofence based on an intersection between the volume and an excluded region. The process remotely controls flight of the unmanned aerial vehicle to the location while ensuring the unmanned aerial vehicle remains with the geofence.

[0031] In one aspect, a computer-implemented process supports use of a heterogenous supply of remote controlled unmanned aerial vehicles for remote operations by end users using a heterogeneous set of end user computing devices. The process involves receiving and storing data describing a geospatial location of launch and capabilities of the unmanned aerial vehicle in response to a request to register an unmanned aerial vehicle. The process selects a registered unmanned aerial vehicle for the remote operation based on the location and parameters of the remote operation in response to a request from an end user computing device to perform a remote operation at a location using an unmanned aerial vehicle. The process transmits command and control data from the end user computing device to the unmanned aerial vehicle selected for the remote operation over a radio network. The process transmits status and streaming media data from the unmanned aerial vehicle selected for the remote operation to the end user computing device over the wide-area network.

[0032] In one aspect, a computer-implemented process supports use of remote controlled unmanned aerial vehicles by multiple users, using a computer system including a cloud service. The process involves receiving an indication of a selected point within an image displayed in a graphical user interface of the user computer from a user computer. The process determines a direction with respect to the drone based on the selected point. The process controls operation of the drone based on the determined direction.

[0033] In one aspect, a computer-implemented process supports use of remote controlled unmanned aerial vehicles by multiple users, for use in a cloud service. The process involves tracking activities performed through the user using one of the unmanned aerial vehicles for each user. The process compares the tracked activities of a first user to tracked activities of a second user or to a threshold for tracked activities to output a comparison result.

[0034] In one aspect, a computer-implemented process supports use of remote controlled unmanned aerial vehicles by multiple users, for use in a cloud service. The process involves monitoring interactions of users with the cloud service. The process transfers cryptocurrency tokens to users based on the monitored interaction of the users with the cloud service.

[0035] In one aspect, a computer-implemented process supports use of remote controlled unmanned aerial vehicles by multiple users using a cloud service. The process involves transitioning a user between communication with the unmanned aerial vehicles and the mobile devices. The process manages transactions associated with the mobile devices.

[0036] In one aspect, a computer-implemented process supports use of remote controlled unmanned aerial vehicles by multiple users using a cloud service. The process involves processing the streaming media data from the unmanned aerial vehicles to identify organisms. The process stores data about identified organisms in a database.

[0037] In one aspect, a computer-implemented process dynamically prices insurance for unmanned aerial vehicle (UAV) operations using a cloud service. The process involves receiving a request for insurance coverage for a UAV operation at a specified location from an end user computing device. The process obtains data describing multiple risk factors associated with the specified location in real-time. The process analyzes the obtained realtime risk factor data to generate a risk profile specific to the requested UAV operation and, based on the generated risk profile, an insurance premium for the requested UAV operation.

[0038] Any of the foregoing aspects may be embodied as a computer system, as any individual component of such a computer system, as a process performed by such a computer system or any individual component of such a computer system, or as an article of manufacture including computer storage in which computer program code is stored and which, when processed by the processing system(s) of one or more computers, configures the processing system(s) of the one or more computers to provide such a computer system or individual component of such a computer system.

[0039] Any of the foregoing can include one or more of the following features. The streaming media data received from the selected one or more unmanned aerial vehicle is transmitted, over the wide-area network, to one or more end-user computing devices. Flight of the selected one or more unmanned aerial vehicles is remotely controlled to the location while ensuring the selected one or more unmanned aerial vehicles remain within an allowable flight zone. When the selected one or more unmanned aerial vehicles comprises a plurality of unmanned aerial vehicles which generate streaming media data from a plurality of different perspectives of the location, the streaming media from the plurality of perspectives are combined into a three-dimensional volumetric model of space around the location. In response to a selected viewpoint within the volumetric space, a view of the event at the location from the selected viewpoint is generated based on the received streamingmedia from the plurality of unmanned aerial vehicles, thereby enabling real-time source of truth evaluation.

[0040] Any of the foregoing can include one or more of the following features. The three- dimensional volumetric model of space comprises a three-dimensional sound stage of the location. When the received streaming media data includes audio streams from a plurality of unmanned aerial vehicles at different respective positions around the location, the received audio streams are synchronized and calibrated to account for differences in distance and input sensitivity between the unmanned aerial vehicles, and the synchronized and calibrated audio streams are combined to generate the three-dimensional sound stage of the location. To combine the synchronized and calibrated audio streams, a time delay of arrival between pairs of audio streams from the plurality of unmanned aerial vehicles is determined, and a direction of arrival for sounds captured in the audio streams is estimated based on the determined time delays. A set of distance equations based on the determined time delays and known locations of the unmanned aerial vehicles is solved to determine a spatial position of a sound source within the three-dimensional sound stage. Beamforming techniques are applied to the combined audio streams to enhance signals from specific directions and suppress noise from other directions within the three-dimensional sound stage. Based on the three- dimensional sound stage and the three-dimensional volumetric model of space around the location, an immersive audio-visual representation of the location is generated. A selected viewpoint within the three-dimensional volumetric model is received, and audio corresponding to the selected viewpoint is rendered based on the three-dimensional sound stage. The remote control of the flight is initiated in response to an event occurring at the location. A plurality of the unmanned aerial vehicles are selected based on the location of the event. The three-dimensional volumetric model is used to, in response to a selected point and camera angle within the volumetric model, generate image data corresponding to the selected point and camera angle from the combined streaming media. The three-dimensional volumetric model is exported to a three-dimensional gaming platform. A geofence defining an allowable flight zone for the unmanned aerial vehicle for the remote operation is generated.

[0041] Any of the foregoing can include one or more of the following features. To specify the volumetric space, a viewshed analysis within the volumetric space is applied to determine a minimum flight altitude to further constrain the geofence by the minimum flight altitude. The excluded region is specified in part by data indicative of restricted airspace. To specifythe geofence, the geofence is further constrained based on the strength of radio signals as received from the unmanned aerial vehicle.

[0042] Any of the foregoing can include one or more of the following features. The cloud service acts as a digital marketplace, facilitating commerce between service providers with a supply of remote UAVs and end-users seeking UAV services. In response to an alert about an event at a location, one or more unmanned aerial vehicles are selected based on the location, flight of the selected unmanned aerial vehicles is remotely controlled to the location, the selected unmanned aerial vehicles are directed to capture and transmit live streaming media from the location over the radio network. The streaming media data from the selected unmanned aerial vehicles is received and stored in computer storage as a source of truth of events occurring at the location. Streaming media data of a plurality of perspectives of the location from the unmanned aerial vehicles is received and stored in computer storage. The streaming media from the plurality of perspectives is combined into a three-dimensional volumetric model of space around the location.

[0043] Any of the foregoing can include one or more of the following features. A geofence defining an allowable flight zone for the unmanned aerial vehicle for the remote operation is generated. To generate the geofence, based on the geospatial location of launch of the registered unmanned aerial vehicle and a specified range for communication between the unmanned aerial vehicle and a remote control for the unmanned aerial vehicle. The geofence is specified based on a volume centered at the geospatial location and size based on the specified range. To generate the geofence, a viewshed analysis within the volume is applied to determine a minimum flight altitude to further constrain the geofence by the minimum flight altitude. To generate the geofence, data indicative of restricted airspace is applied to the geofence to further constrain the geofence based on the restricted airspace. To generate the geofence, the geofence is further constrained based on strength of radio signals as received from the unmanned aerial vehicle. During performance of the remote operation, the unmanned aerial vehicle is controlled to ensure the vehicle remains within the allowable flight zone.

[0044] Any of the foregoing can include one or more of the following features. In response to a request to register an unmanned aerial vehicle, the unmanned aerial vehicle is authenticated with the system. To authenticate the unmanned aerial vehicle, an authentication token is received from an authenticated drone operator, the authentication token vouching for the legitimacy of the unmanned aerial vehicle. To authenticate the unmanned aerial vehicle, acertificate is received from the unmanned aerial vehicle, the certificate signed by a certificate authority, and the certificate is validated. During operation of the unmanned aerial vehicle, a secure connection is established with the unmanned aerial vehicle, a shared secret is received from the unmanned aerial vehicle with each request over the secure connection, and the shared secret is confirmed against an expected value.

[0045] Any of the foregoing can include one or more of the following features. To determine a direction in which to direct the drone, a geographic location in an environment of the drone is determined based on a user input with respect to an image of the environment. The operation comprises changing an orientation of a camera to direct a lens of the camera in the determined direction. The operation comprises moving the drone in the determined direction.

[0046] Any of the foregoing can include one or more of the following features. Reward data is associated with a user based on a comparison between usage by the user and usage by another user. The reward data comprises an amount of cryptocurrency tokens.

[0047] Any of the foregoing can include one or more of the following features. Transactions by a user with an entity at a geographic location of a drone can be managed by the cloud service. Transactions between users can be managed by the cloud service. Managing transactions can include transferring cryptocurrency tokens between the user and the entity or between users.

[0048] Any of the foregoing can include one or more of the following features. Features describing appearance and characteristics of an organism are determined by processing visual data in video frames of the streaming media. To determine the features, neural radiance fields are used. The organism is classified based on the determined features using a classifier. The classifier can include a trained classifier having neural radiance field data as an input. A comparison of the determined features with features of registered organisms in the database is performed. A new record is created or an existing record is updated in the database based on the comparison. The record includes species information. The record further includes at least one of the determined features, GPS coordinates, date, time, or contributor information.

[0049] Any of the foregoing can include one or more of the following features. Data indicating an insurance premium is transmitted to the end user computing device and, in response to receiving acceptance of the insurance premium from the end user computing device, insurance coverage for the UAV operation is activated. During execution of the UAV operation, real-time flight data is received from the UAV and the real-time flight data is analyzed to dynamically update the risk profile and adjust the insurance premium based onthe updated risk profile. During execution of the UAV operation, a record of the UAV operation, including the real-time flight data and any incidents, is maintained. The maintained records of UAV operations, including the real-time flight data and any incidents, are accessed for use in the analysis to generate risk profiles.

[0050] Any of the foregoing aspects can be combined with one or more of the foregoing aspects, thereby providing an drone system with more features. Any of the foregoing features can be used in combination of one or more of the foregoing features.

[0051] The following Detailed Description references the accompanying drawings which form a part of this application, and which show, by way of illustration, specific example implementations. Other implementations may be made without departing from the scope of the disclosure.BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 provides an overview of a system supporting remote drone flights.

[0053] Figure 2 is an illustration of the components of the UAV-as-a-Service Platform

[0054] Figure 3 is an illustration of the components for auto-generation of the UAV geofence.

[0055] Figure 4 is an illustration of a UAV array used for creating a volumetric audio and video space, representative of the sample terrain.

[0056] Figure 5A illustrates an example user interface for an end user computing device.

[0057] Figure 5B illustrates an example configuration for data transmission.

[0058] Figure 6 is a block diagram of an example general purpose computer.DETAILED DESCRIPTION

[0059] The following sections describe examples of enhancements that can be made to systems that support remote drone flights. Remote drone flights are described in the following patent applications or publications, which are hereby incorporated by reference: a. U.S. Patent Publication 2023 / 0136508, entitled “Systems and Methods to Preserve Wildlife and Enable Remote Wildlife Tourism”, and b. PCT Publication WO / 2024 / 091926, entitled “Enhanced Functions of an Unmanned Aerial Vehicle Including Audio Synchronization and Geofencing.”

[0060] Figure 1 depicts an example system for streaming audio and video from a remote destination navigated by an end user-controlled drone. This Figure 1 thus illustrates anexample operating environment in which the various improvements explained herein can be used. An unmanned aerial vehicle (UAV) 100 is equipped with a video camera, which captures streaming video data and still images, and optionally a microphone, which captures streaming audio data. The UAV 100 transmits captured media data over a radio network.

[0061] One class of unmanned aerial vehicles (UAV) are quadcopter drones. Drones have become commonplace for use by both businesses and consumers. Drones with quiet propellers help reduce the impact of noise in the location in which the drone is flying. Drones typically are equipped with digital imaging devices that produce high-definition digital pictures and video. Such drones can have controllable optics, such as zoom lenses. These digital images can be stored on the drone’s on-board storage units and can be transmitted over a radio network link back to a remote controller device. Some drones are equipped with batteries that support long flight times. Some also include image recognition and collision avoidance systems. Commercial entities can use these drones and their imaging capabilities in dangerous or physically challenging environments, such as investigating an advancing forest fire, checking for leaks in a gas pipeline, or active surveillance and pursuit of wildlife poachers.

[0062] A remote controller device 101 communicates with the UAV (100) over the radio network and acts as a network bridge between the UAV (100) and a wide-area-network 102 such as the Internet. The remote control device 101 can be operated by a computer or manually by an individual and may be stationary or mobile. A communication system such as Starlink or 5G cellular enables ubiquitous and remote availability, and low latency, communication, while also supporting bandwidth that provide quality streaming video (e.g., greater than 5 Mbps).

[0063] A management system 103 is an in-network component that manages, provisions, and stores data and credentials used by the operations of this system. The management system 103 may include or may exchange data with one or more additional processing systems 106, such as a computer system that computes geofences or performs other data processing. These and other examples are explained in more detail below.

[0064] An end user computing device 104, such as a personal computer or mobile device, connected to the wide area network 102, executes a software application. Among other things, the software application can enable an end user to access the system, select a UAV 100 for remote control, and transmits user-control information to the selected UAV 100. The end user computing device 104 has access to a communications channel, through the proxyprovided by the remote controller device 101 and over the WAN 102, to receive streaming media from the UAV 100. There may be multiple end user computing devices.

[0065] Some implementations also include an audio guide computing device 105, such as a personal computer or mobile device, which has a network connection to the wide area network 102. Similar to the end user computing device, the device 105 also has access to a communications channel, through the proxy provided by the remote controller device 101 and over the WAN 102, to receive streaming media from the UAV 100. Additionally, this software application transmits supplemental audio data to one or more of the end user computing devices 104, the management system 103, additional processing system 106, or the remote controller device 101. A user of the audio guide computing device 105 typically is a kind of narrator who provides audio commentary about or related to the streaming media received from the UAV 100 or to the other data provided by the management system 103 or additional processing system 106. The audio guide computing device can be configured to take control of navigating the drone from the end user computing device to avoid a hazardous situation. The audio guide computing device also can receive other data over the WAN 102 from the management system 103 or processing system 106. In some embodiments, a multiplicity of interconnected audio guide computing devices 105 can be used. Embodiments with the audio guide extend the experience based on the drone-transmitted video with the addition of an audio communications channel over a WAN that is accessible by the remote controller device, the end user device, and one or more other computing devices used primarily for broadcasting supplemental audio data that is synchronized and multiplexed with the drone’s transmitted video streams.

[0066] In a configuration such as in Figure 1, the remote controller device 101 acts as a network bridge between the attached radio network of UAV 100 and a wide-area-network (WAN) 102. In this case, the UAV's transmitted real-time image data is proxied by the remote controller over the WAN to a remote end user device 104. The end user can view this real-time video data from the end user device 105 and transmit control information, such as changes to the flight path or camera direction and focus, back to the drone bridged by the remote controller.

[0067] The following detailed description explains several enhancements for use in environments providing remote access to one or more UAVs, like the one described in connection with Figure 1. Such enhancements can be used alone or in combination with each other. Such enhancements include, but are not limited to:a. A system using streaming media from drones to provide a "source of truth" about events occurring at remote locations. b. A system for generating a volumetric 3D model of a space around a location by combining streaming media from multiple drones, such as a sound stage. c. An automated geofencing system for drones, based on one or more factors such as launch location, range, viewshed analysis, or restricted airspace. d. A system providing access to drones for end users seeking remote drone services for various purposes. e. A drone control system with a graphical user interface enabling a “click-to- focus” function, allowing end users to invoke operations of the drone by selecting a point in a displayed image. A cryptocurrency-based system to reward end users for various tasks performed using drones. f. A system that gamifies drone usage by tracking and comparing usage by end users. g. A cryptocurrency -based system to reward end users for various tasks performed using drones. h. A system the enables remote shopping using drones. i. A system that tracks user encounters with and identification of organisms in nature. j . A micro-insurance system with real-time risk analysis based on real-time data from drone services.

[0068] UAV as a Service

[0069] Figure 2 illustrates hardware and network components involved in supporting a UAV service. The system includes a multiplicity of UAVs devices (201), each including a suite of sensors for capturing telemetric information, images (still and temporal, optical and thermal) and audio; a radio transmission module that can transmit UAV sensor data and receive command and control data; and a processing unit that can process sensor, telemetric, and command and control data. Though the term “drones” is used by way of example in this description, the term is not meant to limit the scope of the invention.

[0070] To initiate the UAV-as-a-Service, the UAV and the drone operator register with the platform’s cloud service (204). The cloud service is implemented using one or more server computers with computer programs implementing the functionality as described herein.Drone registration is often facilitated by a platform application installed on the drone, the drone’s remote control (202), or any other device that supports an IP -based communications channel to the drone.

[0071] For a drone to be accepted by the platform, the drone supports a minimum set of capabilities. A Drone Capabilities and Services Discovery Protocol can be used to query information from the drone such as manufacturer and brand, a unique identifier such as serial number, and supported sensor information. An example drone JSON response is shown in Table I below."manufacturer": "DJI","brand": “Mavic 3 Thermal",“serial number”: “XJ118MNAW124077”“weight”: “920 g”"capabilities" : {"max speed" : "15 m s"."max take-off altitude" : "6000 meters",“max flight distance ”: “32 km ”,"battery life": "45 minutes","payload capacity" : """sensors": {"wide camera": {"max image size ": ”8000x6000 ”,“image formal ”: “JPEG”,"video resolution" : " 3840x2160@30fps",“video format”: “MPEG-4 AVC”“Thermal Camera” :{"max image size ": "640x512 ",“image formal ”: “JPEG”,"video resolution ": "640x512@30fps ",“video format”: “MPEG-4 AVC”“GNSS”:{“method”: “GPS+Galileo+BeiDou+GONASS ”, “current location” : “42.361145, -71.057083 ” “number satellites “12 ”Table I

[0072] If the drone supports the minimum set of capabilities for the UAS-as-a-Service platform, then the drone can be registered for use on the platform.

[0073] In addition, the drone operator registers with the platform’s cloud service (204). This process can also be expressed through the platform application where the drone operator initiates the registration process and is presented with an identification and password query. The platform binds operation of the drone, for purposes of UAV-as-a-Service, to the registered drone operator.

[0074] A remote controller unit 202 includes a radio module that communicates data with the UAV (201). The remote controller unit 202 also can include a graphical user interface that displays the UAV telematic and sensor data including capture digital images, and another radio transmission module that maintains a data link to a network gateway appliance (203). The remote controller unit 202 also includes a physical interface ergonomically designed to capture UAV pilot hand gestures. The remote controller unit 202 also includes a processing unit that translates the UAV pilot physical gestures into UAV command and control data and enables a resident service for proxying the data from the UAV radio transmission to and from the network appliance (203). In some embodiments, the remote controller unit supports 1) a microphone for capturing audio commands or narration from the UAV pilot and 2) a speaker for rendering audio data to the UAV pilot.

[0075] A network appliance 203 acts as a gateway between the local network spanning the local UAV and remote controller and a WAN, such as the world-wide-web (WWW). Access to the WAN is proxied by, for example, an internet service provider such as a telecom, i.e., a 4G or 5G mobile carrier, a satellite service provider, or fixed line cable provider.

[0076] In some embodiments, the UAV can directly communicate to a WAN using a resident radio module, bypassing the remote controller unit 202. In this scenario, the remote controllerfunctions as an edge-computing device addressable by the cloud service (204) on the wide- area-network. Thus, a drone operator need not be handling a remote controller unit 202; instead the controls can be sent to the UAV from an edge computing device.

[0077] A cloud service (204) provides services for 1) enabling a marketplace of available UAV services including payment handling, payment processing, resource management, authentication and identification services, analytics, and reporting, 2) remoting the UAV experience to end-users, and 3) enhancing drone services with data augmentation such as a flight navigation map, object detection and identification, etc. The cloud server (204) is addressable on a WAN, such as the internet.

[0078] Edge computing devices (205) include a processing unit, a graphical display, a physical interface(s) such as a keyboard, and network addressable by the cloud service (204). Using a resident application on such devices, a human can access the UAV sensor, telemetric, and command and control data transmitted by the UAV (201) via the remotecontrol unit (202), to the network appliance (203), to the cloud server (204), and to the edge computing device (205). For example, displayed on the edge computing device (205) would be a cockpit view of the real-time video captured by the UAV.

[0079] The edge computing device often contains a speaker and a microphone. The microphone captures human voice for transmission by the edge computing device (205) to the cloud server (204) for rebroadcast to other edge computing devices including the remote controller unit (202). The speaker on the edge computing devices (205) allows the rendering of broadcast audio transmitted from other edge computing devices including the remote controller unit (202).

[0080] This separate audio transmission channel allows all participants involved in the UAV service to communicate between and among themselves. An example is a drone service providing a virtual wildlife safari for remote customers. The drone pilot, acting as a wildlife guide, communicates with the customers using an established audio channel. These communications can consist of tour narration of the drone’s broadcast video, navigation suggestions, a notification of a wildlife sighting, etc. In another embodiment, one of the cloud or edge-computing services contains a natural language Al-based application that can identify wildlife and communicate their location to other participants. In some implementations, techniques can be used as described in PCT Publication WO / 2024 / 091926 entitled “Enhanced Functions of an Unmanned Aerial Vehicle Including Audio Synchronization and Geofencing.”

[0081] Moreover, this audio service can be performed by any edge-computing device, addressable by the cloud server and equipped with a microphone and speaker. This technology enables a business model and technical decoupling between UAV, UAV Pilot, and audio guide. This allows the end-user in the UAV-as-a-Service marketplace to separately select a UAV, located in a specific location, a drone operator, and an audio guide.

[0082] All data streams to and from the end-computing devices are proxied by the cloud service (204). The end-user computing devices go through an authorization process to receive the data streams. In some embodiments, for the device to be granted authorization, a financial arrangement is first executed between the end user consuming the data through an application on the end-user computing device, and the UAV service provider.

[0083] When a drone service is requested by end user, both the drone and drone operator are first authenticated by the platform to prevent unauthorized or illegitimate access and usage. The drone operator initiates this authentication using the platform application which maintains a secure, encrypted network connection to the authentication server. The user responds to the authentication server's password challenge and if correct, the server returns an access token, a cryptographically signed message that includes the user’s identity and the token’s duration of validity. This access token may also include additional information such as source IP address, MAC address, drone serial number, etc. All subsequent communications between the platform and application include this authentication token and is checked for validity, i.e., time valid, with each request.

[0084] The protocol for drone authentication can occur using several methods. For example, drone operator “vouching” can be used, where the authenticated drone operator “vouches” for the device as legitimate using their drone operator authenticated credentials. After a drone operator is authenticated, the token provided is included in all messages sent by drone as a means of validating identity. As another example, instead of authentication tokens, the drone authenticates itself using a certificate signed by a certificate authority. As yet another example, a drone can provide a shared secret with each request over a secure, SSL connection. This secret is used with every request and confirmed against an expected value. And as another example, public / private key encryption can be used, where drone messages are signed on the drone’s private key and validated by the authentication server using the drone’s public key.

[0085] Safe Flying Zone Generation

[0086] After a drone and its associated drone operator have registered with the UAS-as-a- Service platform, generation of a three-dimensional airspace map, representing the drone’s safe flying zone, is performed. The boundary of this zone is referred to as a geofence which acts as a navigation boundary for the UAS-as-a-Service drone. In an example implementation, auto-generating the safe flying zone is based on the geospatial location of the drone’s launch location, contained in the Drone Capabilities and Services Discovery Protocol response or obtained through other channels such as a business agreement with the drone operator, and digital elevation model (DEM) data for the region surrounding the launch location. An example implementation of a methodology for defining the allowable airspace is illustrated in Figure 3 and explained in more detail below. Other geofencing techniques, such as those described in PCT Publication WO / 2024 / 091926, also can be used in some implementations.

[0087] A geographic information systems (GIS) application takes as input the drone’s launch location, maximal range as specified by the drone manufacturer or other means, and the DEM data local to the drone’s launch location. The application outputs a volume, such as a cylindrical space (301) above the terrain map, centered at the drone’s launch location (302) with radius equal to the drone’s maximal range. Other polyhedron’s approximating a similar or other three-dimensional shapes based on the location and range of the drone can be used. The upper boundary limit is often defined by the local aviation authority to segment drone airspace from other forms of aviation traffic.

[0088] This volume 301 is then used as input for a viewshed analysis illustrated by the graph 304. To maintain continuous communications, the radio transmission link between drone and remote controller unit must be free of obstructions, such as physical occlusions due to changes in the terrain and destructive signal interference due to reflections off the terrain which can be approximated as height of the first Fresnel zone. The viewshed analysis determines the minimum flight altitude above the ground (AGL), shown by the top, dashed line in graph 304 to maintain unobstructed transmission given the terrain, as indicated by the solid, bottom line in graph 304. In graph 304, the x axis represents the distance from remote controller from the drone in a given direction, and the y axis represents the altitude. Additional clearance between the terrain and the minimum flight altitude can be added to avoid flying too low and disturbing people and wildlife.

[0089] Note that the terrain can be determined from measurements based on sensors from the drone, whether in real-time or from post-flight processing of data from other flights in anarea, or can be based on other data such as the Shuttle Radar Topography Mission (SRTM) topographic data set.

[0090] The maximum geofence radius can be constrained by limiting its range in regions where the drone’s emitted signal power is insufficient to support a high-quality video transmission signal. This limit will be a function of the drone’s transmission power (often regulated by local government), the drone’s transmission frequency, the distance to the remote controller, digital compression technologies including forward error correction coding, and a subjective assessment of the minimum video quality sufficient for the service offering.

[0091] Drone airspace restrictions (represented by the area 303) are often imposed by aviation authorities to limit drone navigation above high traffic or sensitive locations such as national parks, dams, stadiums, utility plants, and government buildings and facilities. Maps of restricted drone airspace are published by governments and other private companies such as ALOFT, OpenSky, etc. Additionally, privacy and property laws in many regions restrict drones from flying over private property. Both sources can be used to refine the geofence, defining the allowable drone airspace.

[0092] In Figure 3, these steps of continued refinement are demonstrated in the resulting geofence 2-dimensional projective mapping illustrated in 300. In this example, the allowable drone flight space is laterally defined by the intersecting of the circular region (301) and the excluded hashed region (303). The lower boundary is illustrated in the top, dashed line in graph 304; the upper boundary is defined by the local regulations to avoid collision with other classes of aircraft, such as planes.

[0093] Further refinement can be done during each drone flight. If the drone navigates into a region where the radio frequency signal does not provide sufficient power reception for maintaining a quality video transmission, the geofence can be updated to be consistent with this region of insufficient RF signal to the remote control.

[0094] An example implementation of some source code for this operation is presented below in Table II. In this implementation, the region is pre-scanned by sampling the radio frequency signal. If the RF signal is below a defined threshold value, i.e., “rf Threshold,” the location becomes part of the geofence boundary. class Point: def init (self, lat, Ion): self.lat = latself.lon = Ion self.signal_strength = None def create_rf_geofence(region_bounds, sampling interval, rf threshold):Algorithm to create a geofence based on RF signal strength measurementsParameters: region bounds: tuple ((min lat, min ion), (max lat, max ion)) sampling interval: float (distance between sampling points in degrees) rf threshold: float (minimum RF signal strength to be inside fence)Returns:List of points forming the geofence boundary II II II# Step 1 : Create sampling grid sampling_points = [](min lat, min ion), (max lat, max ion) = region bounds for lat in range_step(min_lat, max lat, sampling interval): for Ion in range_step(min_lon, max ion, sampling interval): point = Point(lat, Ion) sampling_points.append(point)# Step 2: Measure RF signal strength at each point for point in sampling_points: point. signal strength = measure rf signal (point) # External function# Step 3: Find boundary points (where signal crosses threshold) boundary _points = [] for point in sampling_points:# Check if point is on the boundary by examining neighbors if is_boundary_point(point, sampling_points, rf threshold): boundary _points.append(point)# Step 4: Order boundary points to form a polygon geofence = create_polygon_from_points(boundary_points)# Step 5: Smooth the geofence to remove noise smoothed geofence = smooth_polygon(geofence) return smoothed geofence def is_boundary_point(point, all_points, threshold): II II IIDetermines if a point is on the boundary by checking if it has neighbors both above and below the threshold II II IIneighbors = get_neighboring_points(point, all_points) has strong = False has weak = False for neighbor in neighbors: if neighbor, si gnal strength >= threshold: has strong = True else: has weak = True if has strong and has weak: return True return False def get_neighboring_points(point, all_points, distance_threshold=0.1): . Returns list of points within distance threshold of given point neighbors = [] for other in all_points: if other != point: dist = calculate_distance(point, other) if dist <= distance threshold: neighbors . append(other) return neighbors def create_polygon_from_points(boundary_points): II II IIOrders boundary points into a polygon. Uses Graham scan or similar algorithm to create convex hull, then refines for concave sections II II II# Implementation of convex hull algorithm ordered_points = graham_scan(boundary_points)# Refine convex hull to allow concave sections where appropriate refined_polygon = refine_concave_sections(ordered_points) return refined_polygon def smooth_polygon(polygon_points, smoothing_factor=0.5): II II IIApplies smoothing to remove noise while preserving general shape.Uses moving average or spline interpolation. II II II smoothed_points = [] window_size = 3 for i in range(len(polygon_points)):# Get window of points centered on current point window = get_window(polygon_points, i, window_size)# Calculate smoothed point using weighted average smoothed_point = calculate_weighted_average(window, smoothing factor) smoothed_points.append(smoothed_point) return smoothed_points def range_step(start, end, step):. Helper function to generate range with floating point steps current = start while current < end: yield current current += stepTable II

[0095] Streaming Media from Drones as “Source of Truth”

[0096] In some implementations, “Trust Verification” is an example of a service that can be offered to end-users of the UAV-as-a-Service platform. Alerts triggered by online news, social media posts, public emergency channels, and the like, can be used to issue a “wake-on- alert” notification message to one or more registered drone(s) in the geographic vicinity pertinent to the alert. If a particular UAV does not support “wake-on-alert,” an alert message can be sent to the drone operator through various communication channels, i.e., SMS, email, phone, application notification, etc. The location where this alert notification would be evoked can be determined, for example, by an artificial intelligence system evaluating the source of the alert or by manual intervention. After the drone receives and acknowledges the wake-on-alert message, the drone accepts command and control commands issued by the platform to fly to the alert location and stream the video of the location to one or more end users subscribing to the service. In some embodiments, the drone operator can fly the drone near the alert location if the drone cannot accept remote command and control actions or local regulations do not allow for fully autonomous drone flight.

[0097] In some implementations, such as for emergency and other situations, a drone can act as a “first responder” for validating the emergency situation and archiving the video stream for subsequent offline analysis, serving as source of documented “truth.” As first responder, the drone streams a live video of the alert location. A human or computer system, such as using artificial intelligence or other media processing techniques, can process the live video stream to assess and appropriately respond to the alert situation. Examples of real-time responses that can be assessed using the UAV-as-a-Service platform include, but are not limited to: how many firetrucks and fireman are needed to extinguish a forest fire, how many people are required to evacuate a location due to a nature disaster, how many securitypersonnel should be sent to a residential break-in. Additional analyses, which may use artificial intelligence techniques, such as facial recognition, object identification (such as distinguishing between two objects), or activity tracking (such as wildlife poaching), can be applied to the video stream by the platform for purposes of monitoring and security, research, public safety, and education.

[0098] Volumetric Models

[0099] In some implementations, multiple drones can be simultaneously alerted and dispatched to the alert location. With a multiplicity of drones at different, but nearby locations, capturing real-time audio and video, a volumetric sound and image stage can be constructed. This service can be used to accurately determine the location of a specific noise, such as the location of a gunshot, car crash, or a roaring lion.

[0100] Turning now to Figure 4, to create a three-dimensional sound stage, it is useful for the drone microphones to be arranged in an efficiently sampled configuration, i.e., linear, circular, or planar. As demonstrated in Figure 4, the location of drone; (401-i) is a distance n from an object which is a sound source (402). The captured audio data is synchronized and calibrated to account for differences in distance and input sensitivity. Signal processing techniques are then used to estimate the direction of arrival of the sound source (402) in the captured audio data.

[0101] The time delay of arrival, Tij between two microphones located at n and q distance from a source (402) is:where c is the speed of sound. With N drones forming the microphone array, there are M microphones pairs whereThe location of the source is determined by solving the M distance equations, Tij, which is typically performed using a least squares estimation and using the location of the drone as the location of its microphone.

[0102] In some implementations, beamforming techniques can be used to enhance the signal coming from specific directions and to suppress noise from other directions, allowing the system to isolate sound sources and improve the spatial accuracy of the 3D audio. The direction of arrival estimates are then used to determine the spatial position of the source.

[0103] Multiple video feeds captured from a multiplicity of drones can also be combined to create a 3D image stage for viewing the alert location from any camera angle and location within the sampled space. This combination is simplified when the drones’ video streams overlap in their field of view. A feature extraction and matching algorithm is used to establish correspondence between points in different images. With the actual location and orientation of each drone camera and the feature mapping across multiple images, a triangulation process can be used to calculate the 3D position of these identified features.

[0104] The volumetric model developed by the drone array, as illustrated by example in Figure 4, can provide enriched UAV-as-a-Service experiences. Viewing an alert location from arbitrary viewpoints can provide additional perspective regarding the alert triggering situation. Objects occluded in one viewing angle may be exposed from another viewpoint, allowing for accurate assessment or factual reporting of the situation.

[0105] In some embodiments, the volumetric model can be imported into a three- dimensional gaming platform or three-dimensional metaverse platform, such as Unreal Engine or NVIDIA Omniverse, which can render the captured volumetric space in a virtual reality setting. The UAV-as-a-Service platform can then offer drone flights in this virtual space that replicates the condition of the real-world as captured by the drone array. Such a virtual space can be updated with real-time real-world updates based on video captured by drones. Thus, in addition to live streaming, the system can provide a metaverse that is continually updated and realistic, enabling virtual tourism, a travel-oriented metaverse, and a kind of metaverse platform.

[0106] Cryptocurrency

[0107] In some implementations, such a drone control system can incorporate a reward system to incentivize end users for performing various tasks using drones. For example, a “Natureverse” system can incentivize and promote conservation of biodiversity. In such an implementation, the system employs a proof-of-nature consensus mechanism and a cryptocurrency, herein called “naturecoin” tokens, to reward custodians of natural habitats and contributors who gather evidence of biodiversity. The system is built upon a governance structure rooted in decentralized autonomous organizations (DAOs), ensuring transparency, fairness, and privacy. The Natureverse system also fosters liquidity and demand for naturecoin tokens by inviting ecologically responsible industries to accept naturecoin tokens in exchange for goods and services, offering incentives to both businesses and customers.

[0108] The Natureverse system can revolutionize how we value and conserve biodiversity. By linking a supply of naturecoin tokens to the total size of nature measured in living organisms in the ecosystem, the Natureverse system presents a sustainable economic model for conservation, founded on a natural incentive system with tokenomics. The ecosystem rewards both habitat custodians for conserving nature and a community of contributors who submit authentic imagery of organisms in nature.

[0109] Users of the Natureverse system include proof-of-nature contributors and habitat custodians. These users play a primary role in the data gathering process, obtaining authentic evidence of organisms using drones, camera traps, or mobile phones. The drones provided as a service can be used to support the Natureverse system.

[0110] In a Natureverse system, real-time streaming of videos that, as an example, are captured from drones continuously flying in and around the conservation physical location are captured. Accompanied by the GPS coordinates of the camera, the video provides a rich source of data on the organisms in the habitat. The additional processing systems (e.g., Figure 1) receive and segment the video, and process the video to detect individual organisms. The system can generate a Neural Radiance Field (NeRF), a 3D representation of the organism for each organism, such as explained in more detail below. Each organism's NeRF can then be trained and compared with existing NeRFs in a registry of the Natureverse system.

[0111] Naturecoin tokens are a form of cryptocurrency associated with the Natureverse system. It serves as a reward for proof-of-nature contributors and habitat custodians who add novel organisms to the ecosystem or provide evidence of the continued existence of organisms already recorded. This dual-incentive model encourages both the discovery of new organisms and the ongoing conservation of existing ones.

[0112] To encourage liquidity and demand for naturecoin tokens, the Natureverse system can allow entities to accept naturecoin tokens in exchange for goods and services. For example, entities from industries such as ecotourism, organic agriculture, sustainable fashion, renewable energy, and more, can allow payment with naturecoin tokens. For example, audio guides for a tour of a location can be paid for in naturecoin tokens. This initiative incentivizes more individuals to use naturecoin tokens, fostering a vibrant and sustainable economic ecosystem that supports biodiversity conservation.

[0113] The governance structure of the Natureverse system can be based on decentralized autonomous organizations (DAOs), which ensure transparency and fairness. DAOs allowcommunity members to participate in decision-making processes, reinforcing the democratic nature of the ecosystem.

[0114] The concept of Biodiversity Net Gain (BNG), where the aim is to leave biodiversity in a better state than before, is encouraged through the Natureverse system. It fosters a greater appreciation and understanding of biodiversity, promoting its importance, and encouraging more people to participate in conservation efforts.

[0115] The Natureverse system presents an approach to incentivizing the conservation of biodiversity. By making conservation economically rewarding, it encourages the preservation and expansion of natural habitats. The approach recognizes the intrinsic value of nature beyond its ability to sequester carbon and promotes a more balanced and diverse natural environment.

[0116] Moreover, by integrating naturecoin tokens into various eco-friendly industries, the Natureverse system fosters a circular economy that supports conservation efforts and promotes sustainable business practices. This integration not only increases the demand for naturecoin tokens but also encourages and incentivizes the conservation of biodiversity.

[0117] The use of naturecoin tokens extends beyond the Natureverse system and penetrates various eco-friendly industries. Businesses in sectors such as ecotourism, sustainable fashion, renewable energy, organic agriculture, and more can adopt naturecoin tokens as a payment method, providing a fixed discount to customers who choose to pay with naturecoin tokens. For example, a remote drone flight in the Natureverse system or at a physical location can be purchased using the naturecoin tokens.

[0118] This use of naturecoin tokens not only stimulates the demand and circulation of naturecoin tokens but also aligns with these industries' missions to promote sustainability and conservation. Moreover, this cooperation amplifies the positive impact of the Natureverse system, resulting in a more sustainable economy that supports biodiversity conservation on a larger scale.

[0119] As the Natureverse system continues to grow and evolve, it can be further integrated with additional industries that align with conservation objectives. The proof-of-nature consensus mechanism can be continually refined, ensuring it remains robust, fair, and effective in incentivizing conservation efforts.

[0120] This technology also can be used in government environmental agencies, conservation NGOs, research institutions, and other organizations working on biodiversityconservation, thus expanding the reach of the Natureverse system, and contributing to global conservation goals.

[0121] Organism tracking

[0122] Such a Natureverse system employs techniques for verifying and authenticating the imagery of organisms. One or more of the example implementations provided herein, or other implementations, can be used for identifying and tracking objects.

[0123] As an example implementation, an object recognition process can begin with the generation of a 3D avatar for an organism. An application facilitates the capturing of video footage via drones, camera traps, or mobile phones. This footage is processed using Neural Radiance Fields (NeRF), a technique that learns the appearance and characteristics of an organism from video frames to create a detailed virtual representation of the organism in its natural environment.

[0124] The next stage is identifying the species depicted in the NeRF avatar. This task can be carried out by machine learning algorithms, such as convolutional neural networks (CNNs), which are trained on species classification datasets, and can determine the species label with high accuracy.

[0125] The Natureverse system then performs individual recognition by comparing the NeRF avatar with the registered avatars in the database of the Natureverse system. Techniques such as feature matching and similarity metrics are employed to find the closest matches among previously recorded avatars of the identified species. This step helps to determine whether a specific individual animal has been previously sighted and recorded.

[0126] After completing the results of the individual recognition process, other conditions can occur.

[0127] For example, the object recognition process may indicate that there has been a sighting of a novel individual animal. If the individual animal has not been seen before, it is considered a new entry. A new record is created in the database of the Natureverse system, including the species information, the NeRF avatar, and additional metadata such as GPS coordinates, time / date, and contributor information.

[0128] As another example, the object recognition process may indicate that the recognized animal is already known in the system, and its existing entry should be updated. If the individual animal has been previously sighted and recorded, the new video frames captured during the current sighting are utilized to retrain and refine the animal's NeRF avatar. Theupdated avatar and associated metadata, including new GPS coordinates and time / date, are appended to the existing entry in the database of the Natureverse system.

[0129] For plants, including trees, the assumption is made that they are immovable. If a plant is identified in a location within the Natureverse system that did not previously contain a plant of the same species, it is considered a novel plant. In this case, similar to the animal process, a new record is created. However, if the plant is in a location that previously contained a plant of the same species, this is considered an update to the existing entry. The NeRF avatar is updated, and new metadata are appended to the existing record.

[0130] These comprehensive verification and authentication processes ensure that the data within the Natureverse system is accurate and reliable, making it an effective tool for promoting and tracking biodiversity conservation.

[0131] With ongoing video capture of the conservation location using drones, as an example, the animal NeRF avatars in the Natureverse system can be updated in real time to provide a digital twin of the physical location.

[0132] Gamification

[0133] Such drone flights, whether performed in a physical space or in a virtual space, when performed with multiple participants, can incorporate elements typically found in games such as competition, challenges, rewards, and point systems to make it more engaging, interactive, and enjoyable.

[0134] Thus, in some implementations, gamification can be introduced in drone flights based on quantifying the number of unique activities encountered during a limited flight duration. The system detects when one of the unique activities occurs during a flight by a user, and tracks the occurrences per user in a database, such as by tracking a number, date, time, location, or other information. This information can be compared to information stored for other users or to thresholds representing goals set by the system for user achievements. These unique activities include, but are not limited to, spotting unique species of fauna and flora, encountering, and reporting a distress animal, and patrol minutes for anti-poaching activities. These achievements can accumulate to redeemable rewards, such as: 1) unlocking new features such as use of thermal imaging or extended flight time, 2) ownership of specially designated badges, avatars and / or NFTs for use in the Natureverse system, or 3) naturecoin tokens for remote shopping, drone flight purchase, and unlocking new avatars and adventures in the drone system.

[0135] Remote Shopping

[0136] In some implementations, the flight platform can provide an ecommerce service, enabling a virtual shopping experience for physical purchase, such local crafts, or virtual products for use with a metaverse offered by local merchants in the region of the drone operations. For example, after a remote drone flight ends, a local shopping guide can transition the video communications from the drone to a mobile device. The local shopping guide streams live from the location moving to and from the local merchants and showcasing their offerings using the mobile devices video capture and live streaming capabilities. If a product purchase is of interest for purchase by the remote end-user, they can communicate to the shopping guide to direct communications to the local merchant to address product queries, negotiate pricing terms, and communicate acceptance of the offer. Upon mutual verbal acceptance from the merchant and remote-end user, the end-user will be redirected to a web page to complete the transaction. In some implementations, a form of electronic payment can be used, which may include use of naturecoin tokens. After the transaction has been validated, the shopping guide will reimburse the merchant, take possession of the product, and take the item to a centralized distribution point for shipping to the end-user.

[0137] Point-and-click to focus

[0138] In some implementations, within a graphical user interface that displays images received from one of the drones, an end-user can select a point in the displayed image. The selection can be done using any input device allowing a point to be selected, such as a touchscreen, mouse, cursor, or other input. In response to a selected point, an operation can be performed. The operation can be selected from a set of possible operations.

[0139] One kind of operation is to direct motion of the drone. The selected point is used to determine a geographical location in the environment of the drone to which the drone will be moved. This geographical location is processed into one or more control commands to the drone to move the drone in the direction towards the determined geographical location.

[0140] Another kind of operation is to control a gimbal which mounts a camera on the drone. The selected point is used to determine a geographical location in the environment of the drone or a direction with respect to the drone. This direction is processed into one or more control commands to the drone to control the gimbal to move the camera to point towards the determined direction. Depending on various factors such as camera zoom and user selection,a combination of gimbal and drone body rotation is used to center the drone’s field of view based on the click command of the user.

[0141] An example set of operations for implementing this click to center solution are as follows. First, a user clicks on a portion of the screen of interest, which is received as an input by a computer. The system computes the yaw and pan angles based on a baseline field width and height and camera zoom factor.

[0142] The system then turns the gimbal (gimbal-yaw) and / or body (body-yaw) in order to center the screen at the point clicked. The system takes into account other factors, such as gimbal field of view, FOV, limitations (pan and yaw); user clicks beyond the gimbal’s FOV; and user clicks that require movements too small for the drone’s gimbal systems.

[0143] The system also can provide “automatic recentering.” That is, the system automatically straightens the body to align with the gimbal so that forward movement is in the direction of the camera. The system will also straighten the body if the user tries to yaw beyond the gimbal limit. In that case, the system will auto-straighten the body to allow for additional yaw without the user having to control.

[0144] Repositioning (drone or camera gimbal) is scaled based on the drone’s zoom factor (focal length) of the drone camera. This allows the user to pan, yaw, and move at a constant visual speed as defined by movement of the screen. The system determines if the repositioning uses a gimbal or drone rotation where the former are more target a finer degree of control.

[0145] An example of this implementation written in the Kotlin Programming language is illustrated in Appendix I which forms a part of this Specification and are hereby incorporated by reference.

[0146] User Interface

[0147] Figure 5 A illustrates an example user interface for an end user computing device. This interface can be displayed in a browser application on the end user computing device that is accessing a drone flight. The bulk of the display area is a live video feed 576. For example, within the display area of the live video, a user can select a point for the “click-to- focus” operation. Also displayed is a map 582 of the location surrounding the drone, the location of the drone, and a geofence around the drone.

[0148] Status information for the flight also can be shown. For example, a height meter 574 indicates the elevation of the drone. A list of nearby points of interest 572 also can bedisplayed. The current flight time 570 can be shown, such as by a bar graph illustrating the time so far versus a total allowed time. If an audio guide is available for the current flight, an image or other indication of the audio guide can be provided as indicated at 580.

[0149] To control the drone, a first dial graphical element provides up / down / rotate controls 578, and a second dial graphical element provides forward / back / left / right controls 588. Camera control 584 allow the end user to change focus, zoom, or other optical controls of the camera. Record button 586 allows a user to start and stop video recording or take a photo.

[0150] Data transmission

[0151] In some implementations, the transmission of audio and video can be managed separately from transmission of telemetry and bi-directional control data. For example, as shown in Figure 5B, a streaming media channel 550, which can include one or more audio or video channels, or combination of these, may be used to transfer streaming media data from the drone’s access point 560 to customer browsers 558 of the wide-area network. For example, one or more WebRTC audio or video (or both) channels provided by a WebRTC service 552 can be used for this purpose. A data-control channel, which can include one or more data channels 554, can be used to transfer drone telemetry data, command and control data, status data, or any other data, including bi-directional data transmission. This data control channel may be managed by a separate control service 556, i.e., separate from the servers processing the streaming media channels over WebRTC. The services 556 and 558 can be implemented on an autoscaling infrastructure, such as by using Amazon Web Services (AWS). The end-to-end system thus transfers streaming media and other data on separate channels from drone to network appliance to cloud to the cloud server to the end-user(s), and back.

[0152] As an example implementation, drones such as the DJI Mavic series Mavic 2, Mavic 2 Zoom, Mavic 3 Enterprise, and Mavic 3 Thermal drones can be used. As a transmission, the DJI Ocusync version 2.0 and 3.0 transmission systems can be used, which operate at 2.4 or 5.8GHz, and provide video quality of 1080p / 30fps, 120ms (version 2.0) or 1080p / 30fps, 50ms (version 3.0). The telemetry and control signals can be transferred using the data control channel service 556 and the video data, and optionally audio data, can be transferred using the WebRTC service 552. The streaming media data can be encoded, such as by using H264, VP8, and VP9 SVC encodings.

[0153] Micro-insurance

[0154] The platform calculates pre-flight insurance premiums by analyzing multiple risk factors in real-time, including location-specific hazards, time-of-day conditions, and planned flight duration. This dynamic risk assessment incorporates variables such as population density, local airspace restrictions, weather patterns, and historical incident data for the selected flight area. The platform's ability to capture real-time flight data enables dynamic pricing that reflects actual risk exposure rather than generalized estimates. By processing these parameters through machine learning algorithms, the system generates optimized coverage terms and pricing specific to each mission's risk profile, enabling operators to secure precisely tailored insurance protection before takeoff. From an operational perspective, embedding insurance within the platform eliminates the need for separate policy management and simplifies documentation requirements. The approach also streamlines the claims process by maintaining all flight records and incident data within a single system. This model represents an evolution in drone risk management, where insurance functions as an integrated operational component rather than a separate administrative process.

[0155] To implement the various enhancements described above, a computer system, including one or more computers, processes one or more computer programs which instruct the processing system of a computer to perform various functions. Each computer typically is, but is not limited to, a general -purpose computer that can be programmed to implement functionality such as described in the example implementations. Examples of a computer include, but are not limited to, a client device, including mobile devices and client computers, one or more server computers, or one or more database computers, or combinations of any two or more of these.

[0156] Figure 6 is a block diagram of a computer which includes a processing system which processes computer programs. Computer programs on such a computer typically include an operating system and one or more applications. The operating system is a computer program running on the computer that manages access to resources of the computer by applications and the operating system. The resources typically include memory, storage, communication interfaces, input devices and output devices.

[0157] Examples of such computers include, but are not limited to, larger computer systems such as server computers, database computers, desktop computers, laptop and notebook computers, as well as mobile or handheld computing devices, such as a tablet computer,handheld computer, smart phone, media player, personal data assistant, audio and / or video recorder, or wearable computing device.

[0158] With reference to Figure 6, an example computer 600 comprises a processing system including at least one processing unit 602 and memory 604. The computer can have multiple processing units 602 and multiple devices implementing the memory 604. A processing unit 602 can include a processing core among a plurality of processing cores (not shown) within a processing device. Such processing cores typically operate independently of each other. One or more coprocessing units 620, such as a graphics processing unit or GPU or programmable logic device, also can be present in the computer. The memory 604 may include volatile devices (such as dynamic random-access memory (DRAM) or other random-access memory device), and non-volatile devices (such as a read-only memory, flash memory, and the like) or some combination of the two, and optionally including any memory available within a processing device. Other memory, such as dedicated memory or registers, also can reside within a processing unit. Such a memory configures is delineated by the dashed line 604 in Figure 6. The computer 600 may include additional storage (removable storage, nonremovable storage, or both) including, but not limited to, solid state devices or magnetically or optically recorded disks. Such additional storage is illustrated in Figure 6 by removable storage 608 and nonremovable storage 610. The various components in Figure 6 are generally interconnected by an interconnection mechanism 630, such as one or more buses or switches or other electrical or optical connections allowing data and instructions to be passed among the components. The computer 600 may also include one or more communications connect! on(s) 612 that allow the computer to communicate with other devices over a communication medium.

[0159] Computer storage media and communication media are mutually exclusive categories of media.

[0160] A computer storage medium is any medium in which data can be stored in and retrieved from addressable physical storage locations by the computer. Computer storage media includes volatile and nonvolatile memory devices, and removable and non-removable storage devices. Memory 604, removable storage 608 and non-removable storage 610 are all examples of computer storage media. Some examples of computer storage media are DRAM, SRAM, and ROM devices, flash memory, solid state storage devices, or other electronic storage technology, CD-ROM, digital versatile disks (DVD), or other optically or magneto-optically recorded storage devices, or magnetic disk storage or other magnetic storage devices.

[0161] Communication media typically propagate a modulated data signal such as a carrier wave or other transport mechanism over or through a wired or wireless substance to transmit information, such as data, data structures, computer program code, program modules or other information, from one device to another device. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. A transmitting device changes its configuration or state to transform the signal generated for transport over the communication medium based on the information to be encoded. A receiving device changes its configuration or state in response to the signal to transform the signal back into the information, i.e., to decode the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, such as electrical, optical, or electro-optical wired connections, and wireless media, which includes any non-wired communication media that allows propagation of signals, such as acoustic, electromagnetic, electrical, optical, infrared, radio frequency and other signals.

[0162] Communications connections 612 are devices, such as a network interface or radio transmitter, which access communication media to transmit data, or receive data, or both transmit and receive data, in signals propagated through the communication media to support communications with other devices. Example communications connections include, but are not limited to, a computer network interface device that provides a wired connection to a computer network, a wireless communication interface for wireless connection to a computer network, or a radio transmitter for telephonic communications over cellular telephone networks. A wired connection typically includes an electrical and mechanical interface to connect physically and electrically to the communication media. For example, a cellular connection, a Wi-Fi connection, a Bluetooth connection, an Ethernet connection, and yet other connections may be present in the computer.

[0163] The computer 600 may have various input device(s) 614 such as one or more various pointer (whether single-pointer or multi-pointer) devices, such as a mouse, tablet and pen, touchpad and other touch-based input devices, or stylus, one or more keypad or keyboard devices, one or more image input devices, such as still and motion cameras, one or more audio input devices, such as a microphone, or other devices, such as a push button device, a switch, a fader, a knob, an audio controller, or other mechanical device that produces a signalresponsive to a user or machine input. Such input devices are well known in the art and need not be discussed at length here.

[0164] The computer may have various output device(s) 616, such as presentation devices, which present visual, audible, tactile or other forms of information to users, such as a display, speakers, printers, and so on, and controlled devices, such as equipment, robotics, machines, or other mechanical, electrical, optical or other devices. Such output devices are well known in the art and need not be discussed at length here.

[0165] In some implementations, one or more of the computer storage 608, 610, communication connection 612, output device 616 and input device 614 can be integrated within a housing of the computer. In some implementations, one or more of such components can be connected to the computer through an interface device in the computer. Such an interface device includes a communication connection, which connects the computer to a communication medium connected to the device, and a processing logic, which controls how the computer receives or transmits signals over the communication medium. The reference numbers 608, 610, 612, 614 and 616 can be understood to either indicate the interface for connection to a device or indicate the device itself, or both.

[0166] The operating system of the computer typically includes one or more computer programs, commonly called a driver, which respectively manages access to a resource, such as computer storage 608 and 610, a communication connection 612, an output device 616, or an input device 614. Such access generally includes managing inputs from and outputs to the device. In the case of communication connections, the operating system also may include one or more computer programs for implementing communication protocols used to communicate information between computers and devices through the communication connections 612.

[0167] Each component (which also may be called an “application”, a “module”, an “engine”, a “computational model”, a “generator”, a “processor” or the like), of a computer system such as described herein, and which operates on one or more computers, can be implemented as computer program code processed by the processing system(s) of one or more computers. Computer program code includes computer-executable instructions and / or computer-interpreted instructions, such as program modules, which instructions are processed by a processing system of a computer. Generally, such instructions define routines, programs, objects, components, data structures, and so on, that, when processed by a processing system, instruct the processing system to perform operations on data or configurethe processor or computer to implement various components or data structures in computer storage. A data structure is defined in a computer program and specifies how data is organized in computer storage, such as in a memory device or a storage device, so that the data can accessed, manipulated, and stored by a processing system of a computer.

[0168] It should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific implementations described above. The specific implementations described above are disclosed as examples only.

[0169] What is claimed is:70727476787980818283848587899092}}) ;100101}102103104}105106107

Claims

CLAIMS1. A computer system for supporting use of remote controlled unmanned aerial vehicles to record streaming media as a source of truth of an event occurring at a location, the computer system comprising: a cloud service, comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network and including a radio for communication data over a radio network to one or more unmanned aerial vehicles, the data including at least command and control data to the one or more unmanned aerial vehicles and status and streaming media data from the one or more unmanned aerial vehicles; wherein the cloud service includes an application running on the cloud service to configure the cloud service to: in response to an alert about an event at a location, select one or more unmanned aerial vehicles based on the location; remotely control flight of the selected one or more unmanned aerial vehicles to the location; direct the selected one or more unmanned aerial vehicles to capture and transmit live streaming media from the location over the radio network; and receive and store the streaming media data from the selected one or more unmanned aerial vehicles in computer storage as a source of truth of events occurring at the location.

2. The computer system of claim 1, wherein the cloud service is further configured to transmit, over the wide-area network, the streaming media data received from the selected one or more unmanned aerial vehicle to one or more end-user computing devices.

3. The computer system of claim 2 wherein the cloud service is further configured to remotely control flight of the selected one or more unmanned aerial vehicles to the location while ensuring the selected one or more unmanned aerial vehicles remain within an allowable flight zone.

4. The computer system of claim 3, wherein the selected one or more unmanned aerial vehicles comprises a plurality of unmanned aerial vehicles generating streaming media datafrom a plurality of different perspectives of the location, the cloud service is further configured to: combine the streaming media from the plurality of perspectives received and stored from the selected one or more unmanned aerial vehicles into a three-dimensional volumetric model of space around the location.

5. The computer system of claim 4, wherein the cloud service is further configured to, in response to a selected viewpoint within the volumetric space, generate a view of the event at the location from the selected viewpoint based on the received streaming media from the plurality of unmanned aerial vehicles, thereby enabling real-time source of truth evaluation.

6. A computer system for supporting use of remote controlled unmanned aerial vehicles to record streaming media of a location to generate a volumetric model of three-dimensional space around the location, the computer system comprising: a cloud service, comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network and including a radio for communication data over a radio network to one or more unmanned aerial vehicles, the data including at least command and control data to the one or more unmanned aerial vehicles and status and streaming media data from the one or more unmanned aerial vehicles; wherein the cloud service includes an application running on the cloud service to configure the cloud service to: remotely control flight of one or more unmanned aerial vehicles to the location; direct the one or more unmanned aerial vehicles to capture and transmit live streaming media from the location; receive and store in computer storage streaming media data of a plurality of perspectives of the location from the one or more unmanned aerial vehicles; and combine the streaming media from the plurality of perspectives from the one or more unmanned aerial vehicles into a three-dimensional volumetric model of space around the location.

7. The computer system of claim 6, wherein the three-dimensional volumetric model of space comprises a three-dimensional sound stage of the location.

8. The computer system of claim 7, wherein the received streaming media data includes audio streams from a plurality of unmanned aerial vehicles at different respective positions around the location, and wherein the cloud service is further configured to: synchronize and calibrate the received audio streams to account for differences in distance and input sensitivity between the unmanned aerial vehicles; and combine the synchronized and calibrated audio streams to generate the three- dimensional sound stage of the location.

9. The computer system of claim 8, wherein, to combine the synchronized and calibrated audio streams, the cloud service is further configured to: determine a time delay of arrival between pairs of audio streams from the plurality of unmanned aerial vehicles; and estimate a direction of arrival for sounds captured in the audio streams based on the determined time delays.

10. The computer system of claim 9, wherein the cloud service is further configured to solve a set of distance equations based on the determined time delays and known locations of the unmanned aerial vehicles to determine a spatial position of a sound source within the three- dimensional sound stage.

11. The computer system of claim 8, wherein the cloud service is further configured to apply beamforming techniques to the combined audio streams to enhance signals from specific directions and suppress noise from other directions within the three-dimensional sound stage.

12. The computer system of claim 8, wherein the cloud service is further configured to generate, based on the three-dimensional sound stage and the three-dimensional volumetric model of space around the location, an immersive audio-visual representation of the location.

13. The computer system of claim 12, wherein the cloud service is further configured to receive a selected viewpoint within the three-dimensional volumetric model, and render audio corresponding to the selected viewpoint based on the three-dimensional sound stage.

14. The computer system of claim 6, wherein the cloud service is configured to initiate the remote control of the flight in response to an event occurring at the location.

15. The computer system of claim 14, wherein the cloud service is configured to select a plurality of the unmanned aerial vehicles based on the location of the event.

16. The computer system of claim 15, wherein the cloud service is further configured to use the three-dimensional volumetric model to, in response to a selected point and camera angle within the volumetric model, generate image data corresponding to the selected point and camera angle from the combined streaming media.

17. The computer system of claim 13, wherein the cloud service is further configured to export the three-dimensional volumetric model to a three-dimensional gaming platform.

18. The computer system of claim 6, wherein the cloud service is further configured to generate a geofence defining an allowable flight zone for the unmanned aerial vehicle for the remote operation.

19. The computer system of claim 18, wherein to generate the geofence the cloud service is further configured to: based on the geospatial location of launch of the registered unmanned aerial vehicle and a specified range for communication between the unmanned aerial vehicle and a remote control for the unmanned aerial vehicle, specify the geofence based on a cylindrical space centered at the geospatial location with a radius based on the specified range and with a maximum flight altitude.

20. The computer system of claim 19, wherein to generate the geofence the cloud service is further configured to apply a viewshed analysis within the cylindrical space to determine a minimum flight altitude to further constrain the geofence by the minimum flight altitude.

21. The computer system of claim 20, wherein to generate the geofence the cloud service is further configured to apply data indicative of restricted airspace to the geofence to further constrain the geofence based on the restricted airspace.

22. The computer system of claim 21, wherein to generate the geofence the cloud service is further configured to constrain the geofence based on strength of radio signals as received from the unmanned aerial vehicle.

23. The computer system of claim 18, wherein the cloud service is further configured to, during performance of the remote operation, ensure the unmanned aerial vehicle remains within the allowable flight zone.

24. A computer system for supporting use of remote-controlled unmanned aerial vehicles to a location within an allowable flight space, the computer system comprising: a cloud service, comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network and including a radio for communication data over a radio network to one or more unmanned aerial vehicles, the data including at least command and control data to the one or more unmanned aerial vehicles and status and streaming media data from the one or more unmanned aerial vehicles; wherein the cloud service includes an application running on the cloud service to configure the cloud service to: based on a geospatial location of launch of an unmanned aerial vehicle and a specified range for communication between the unmanned aerial vehicle and a remote control for the unmanned aerial vehicle, specify a volumetric space centered at the geospatial location with a radius based on the specified range and with a maximum flight altitude; define the geofence based on an intersection between the volumetric space and an excluded region; and remotely control flight of the unmanned aerial vehicle to the location while ensuring the unmanned aerial vehicle remains with the geofence.

25. The computer system of claim 24, wherein to specify the volumetric space the cloud service is further configured to apply a viewshed analysis within the volumetric space todetermine a minimum flight altitude to further constrain the geofence by the minimum flight altitude.

26. The computer system of claim 25, wherein the excluded region is specified in part by data indicative of restricted airspace.

27. The computer system of claim 26, wherein to specify the geofence the cloud service is further configured to constrain the geofence based on strength of radio signals as received from the unmanned aerial vehicle.

28. A computer system for supporting use of a heterogenous supply of remote controlled unmanned aerial vehicles for a remote operation by end users using a heterogeneous set of end user computing devices, the computer system comprising: a cloud service, comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network and including a radio for communication data over a radio network to one or more unmanned aerial vehicles, the data including at least command and control data to the one or more unmanned aerial vehicles and status and streaming media data from the one or more unmanned aerial vehicles; wherein the cloud service includes an application running on the cloud service to configure the cloud service to: in response to a request to register an unmanned aerial vehicle, receive and store data describing a geospatial location of launch and capabilities of the unmanned aerial vehicle; in response to a request from an end user computing device to perform a remote operation at a location using an unmanned aerial vehicle, select a registered unmanned aerial vehicle for the remote operation based on the location and parameters of the remote operation; transmit, over the radio network, command and control data from the end user computing device to the unmanned aerial vehicle selected for the remote operation; and transmit, over the wide-area network, status and streaming media data from the unmanned aerial vehicle selected for the remote operation to the end user computing device.

29. The computer system of claim 28, wherein the cloud service acts as a digital marketplace, facilitating commerce between service providers with a supply of remote UAVs and endusers seeking UAV services.

30. The computer system of claim 28, wherein the cloud service is further configured to: in response to an alert about an event at a location, select one or more unmanned aerial vehicles based on the location; remotely control flight of the selected one or more unmanned aerial vehicles to the location; direct the selected one or more unmanned aerial vehicles to capture and transmit live streaming media from the location over the radio network; and receive and store the streaming media data from the selected one or more unmanned aerial vehicles in computer storage as a source of truth of events occurring at the location.

31. The computer system of claim 28, wherein the cloud service is further configured to: remotely control flight of one or more unmanned aerial vehicles to the location; direct the one or more unmanned aerial vehicles to capture and transmit live streaming media from the location; receive and store in computer storage streaming media data of a plurality of perspectives of the location from the one or more unmanned aerial vehicles; and combine the streaming media from the plurality of perspectives from the one or more unmanned aerial vehicles into a three-dimensional volumetric model of space around the location.

32. The computer system of claim 28, wherein the cloud service is further configured to generate a geofence defining an allowable flight zone for the unmanned aerial vehicle for the remote operation.

33. The computer system of claim 32, wherein to generate the geofence, the cloud service is further configured to: based on the geospatial location of launch of the registered unmanned aerial vehicle and a specified range for communication between the unmanned aerial vehicle and a remotecontrol for the unmanned aerial vehicle, specify the geofence based on a volume centered at the geospatial location and size based on the specified range.

34. The computer system of claim 33, wherein to generate the geofence the cloud service is further configured to apply a viewshed analysis within the volume to determine a minimum flight altitude to further constrain the geofence by the minimum flight altitude.

35. The computer system of claim 34, wherein to generate the geofence the cloud service is further configured to apply data indicative of restricted airspace to the geofence to further constrain the geofence based on the restricted airspace.

36. The computer system of claim 35, wherein to generate the geofence the cloud service is further configured to constrain the geofence based on strength of radio signals as received from the unmanned aerial vehicle.

37. The computer system of claim 36, wherein the cloud service is further configured to, during performance of the remote operation, ensure the unmanned aerial vehicle remains within the allowable flight zone.

38. The computer system of claim 28, wherein, in response to a request to register an unmanned aerial vehicle, the cloud service is further configured to authenticate the unmanned aerial vehicle with the system.

39. The computer system of claim 38, wherein to authenticate the unmanned aerial vehicle the cloud service is further configured to receive an authentication token from an authenticated drone operator, the authentication token vouching for the legitimacy of the unmanned aerial vehicle.

40. The computer system of claim 38, wherein to authenticate the unmanned aerial vehicle the cloud service is further configured to receive a certificate from the unmanned aerial vehicle, the certificate signed by a certificate authority, and validate the certificate.

41. The computer system of claim 28, wherein during operation of the unmanned aerial vehicle the cloud service is further configured to establish a secure connection with the unmanned aerial vehicle, receive a shared secret from the unmanned aerial vehicle with each request over the secure connection, and confirm the shared secret against an expected value.

42. The computer system of claim 28, wherein the cloud service is further configured to receive messages from the unmanned aerial vehicle signed with a private key of the unmanned aerial vehicle, and validate the signed messages using a public key of the unmanned aerial vehicle.

43. The computer system of claim 26, wherein during operation of the unmanned aerial vehicle the cloud service is further configured to include the authentication information with all subsequent communications from the unmanned aerial vehicle and validate the authentication information with each communication.

44. A computer system for supporting use of remote-controlled unmanned aerial vehicles by multiple users, the computer system comprising: a cloud service, comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network and including a radio for communication data over a radio network to one or more unmanned aerial vehicles, the data including at least command and control data to the one or more unmanned aerial vehicles and status and streaming media data from the one or more unmanned aerial vehicles; wherein the cloud service includes an application running on the cloud service to configure the cloud service to: receive, from a user computer, an indication of a selected point within an image displayed in a graphical user interface of the user computer; determine a direction with respect to the drone based on the selected point; and control operation of the drone based on the determined direction.

45. The computer system of claim 44, wherein to determine the direction, the cloud service is configured to determine a geographic location in an environment of the drone.

46. The computer system of claim 44, wherein the operation comprises changing an orientation of a camera to direct a lens of the camera in the determined direction.

47. The computer system of claim 44, wherein the operation comprises moving the drone in the determined direction.

48. A computer system for supporting use of remote-controlled unmanned aerial vehicles by multiple users, the computer system comprising: a cloud service, comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network and including a radio for communication data over a radio network to one or more unmanned aerial vehicles, the data including at least command and control data to the one or more unmanned aerial vehicles and status and streaming media data from the one or more unmanned aerial vehicles; wherein the cloud service includes an application running on the cloud service to configure the cloud service to: for each user, track activities performed through the user using one of the unmanned aerial vehicles; and compare the tracked activities of a first user to tracked activities of a second user or to a threshold for tracked activities to output a comparison result.

49. The computer system of claim 48, wherein the cloud service is further configured to associate reward data with the user based on the comparison result.

50. The computer system of claim 49, wherein the reward data comprises an amount of cryptocurrency tokens.

51. A computer system for supporting use of remote-controlled unmanned aerial vehicles by multiple users, the computer system comprising: a cloud service, comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network and including a radio for communication data over a radio network to one or more unmanned aerial vehicles, the data including at least command and control data to the one or moreunmanned aerial vehicles and status and streaming media data from the one or more unmanned aerial vehicles; a cryptocurrency system supporting cryptocurrency tokens connected to the wide-area network; wherein the cloud service includes an application running on the cloud service to configure the cloud service to: access the cryptocurrency system; monitor interactions of users with the cloud service; and transfer cryptocurrency tokens to users based on the monitored interactions of the users with the cloud service.

52. A computer system for supporting use of remote controlled unmanned aerial vehicles by multiple users, the computer system comprising: a cloud service, comprising: at least one server computer connected to a wide-area network; at least one network appliance connected to the wide-area network and including a radio for communicating UAV data over a radio network to one or more unmanned aerial vehicles, the UAV data including at least command and control data to the one or more unmanned aerial vehicles and status and streaming media data from the one or more unmanned aerial vehicles; at least one mobile device appliance connected to the wide-area network and including a radio for communicating mobile data over a communication network to one or more mobile devices, the mobile data including at least voice or text data to the one or more mobile devices and video or audio or text data from the one or more mobile devices; wherein the cloud service includes an application running on the cloud service to configure the cloud service to: transition a user between communication with the one or more unmanned aerial vehicles and the one or more mobile devices; and manage transactions associated with the one or more mobile devices.

53. The computer system of claim 52, wherein to manage transactions, the cloud service is further configured to transfer cryptocurrency tokens.

54. A computer system for supporting use of remote-controlled unmanned aerial vehicles by multiple users, the computer system comprising: a cloud service, comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network and including a radio for communication data over a radio network to one or more unmanned aerial vehicles, the data including at least command and control data to the one or more unmanned aerial vehicles and status and streaming media data from the one or more unmanned aerial vehicles; wherein the cloud service includes an application running on the cloud service to configure the cloud service to: process the streaming media data from the unmanned aerial vehicles to identify organisms; and store data about identified organisms in a database.

55. The computer system of claim 54, wherein to process the streaming media data, the cloud service is configured to determine features describing appearance and characteristics of the organism from visual data in video frames of the streaming media.

56. The computer system of claim 55, wherein to determine the features, the cloud service is configured to use neural radiance fields.

57. The computer system of claim 56, wherein to process the streaming media data, the cloud service is configured to classify the organism.

58. The computer system of claim 57, wherein the classifier comprises a trained classifier having neural radiance field data as an input.

59. The computer system of claim 58, wherein the cloud service is configured to perform a comparison of the determined features with features of registered organisms in the database.

60. The computer system of claim 59, wherein the cloud service is configured to create a new record or update an existing record in the database based on the comparison.

61. The computer system of claim 60, wherein the record includes species information.

62. The computer system of claim 61, wherein the record further includes at least one of the determined features, GPS coordinates, date, time, or contributor information.

63. A computer system for dynamically pricing insurance for unmanned aerial vehicle (UAV) operations, the computer system comprising: a cloud service, comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network and including a radio for communication data over a radio network to one or more unmanned aerial vehicles, the data including at least command and control data to the one or more unmanned aerial vehicles and status and streaming media data from the one or more unmanned aerial vehicles; wherein the cloud service includes an application running on the cloud service to configure the cloud service to: receive, from an end user computing device, a request for insurance coverage for a UAV operation at a specified location; obtain, in real-time, data describing multiple risk factors associated with the specified location, the risk factors including at least location-specific hazards, time-of-day conditions, local airspace restrictions, current weather conditions, and historical incident data for the specified location; and analyze the obtained real-time risk factor data to generate a risk profile specific to the requested UAV operation and, based on the generated risk profile, an insurance premium for the requested UAV operation.

64. The computer system of claim 63, wherein the application running on the cloud service is further configured to: transmit data indicating the insurance premium to the end user computing device; and in response to receiving acceptance of the insurance premium from the end user computing device, activate insurance coverage for the UAV operation.

65. The computer system of claim 63, wherein the application running on the cloud service is further configured to, during execution of the UAV operation, to receive real-time flight datafrom the UAV and analyze the real-time flight data to dynamically update the risk profile and adjust the insurance premium based on the updated risk profile.

66. The computer system of claim 65, wherein the application running on the cloud service is further configured to, during execution of the UAV operation, to maintain a record of the UAV operation, including the real-time flight data and any incidents.

67. The computer system of claim 66, wherein the application running on the cloud service is further configured to access maintained records of UAV operations, including the real-time flight data and any incidents, for use in the analysis to generate risk profiles.

68. A computer-implemented process for supporting use of remote controlled unmanned aerial vehicles to record streaming media as a source of truth of an event occurring at a location, the computer-implemented process comprising: in response to an alert about an event at a location, selecting one or more unmanned aerial vehicles based on the location; remotely controlling flight of the selected one or more unmanned aerial vehicles to the location; directing the selected one or more unmanned aerial vehicles to capture and transmit live streaming media from the location over the radio network; and receiving and storing in computer storage the streaming media data from the selected one or more unmanned aerial vehicles in computer storage as a source of truth of events occurring at the location.

69. The computer-implemented process of claim 68, further comprising transmitting, over the wide-area network, the streaming media data received from the selected one or more unmanned aerial vehicle to one or more end user computing devices.

70. The computer-implemented process of claim 69, further comprising remotely controlling flight of the selected one or more unmanned aerial vehicles to the location while ensuring the selected one or more unmanned aerial vehicles remain within an allowable flight zone.

71. The computer-implemented process of claim 70, wherein the selected one or more unmanned aerial vehicles comprises a plurality of unmanned aerial vehicles generating streaming media data from a plurality of different perspectives of the location, and the computer-implemented process further comprises: combining the streaming media from the plurality of perspectives received and stored from the selected one or more unmanned aerial vehicles into a three-dimensional volumetric model of space around the location.

72. The computer-implemented process of claim 71, further comprising, in response to a selected viewpoint within the volumetric space, generating a view of the event at the location from the selected viewpoint based on the received streaming media from the plurality of unmanned aerial vehicles, thereby enabling real-time source of truth evaluation.

73. A computer-implemented process for supporting use of remote controlled unmanned aerial vehicles to record streaming media of a location to generate a volumetric model of three-dimensional space around the location, the computer-implemented process comprising: remotely controlling flight of one or more unmanned aerial vehicles to the location; directing the one or more unmanned aerial vehicles to capture and transmit live streaming media from the location; receiving and storing in computer storage streaming media data of a plurality of perspectives of the location from the one or more unmanned aerial vehicles; and combining the streaming media from the plurality of perspectives from the one or more unmanned aerial vehicles into a three-dimensional volumetric model of space around the location.

74. The computer-implemented process of claim 73, wherein the three-dimensional volumetric model of space comprises a three-dimensional sound stage of the location.

75. The computer-implemented process of claim 74, wherein the received streaming media data includes audio streams from a plurality of unmanned aerial vehicles at different respective positions around the location, and the combining the streaming media comprises: synchronize and calibrate the received audio streams to account for differences in distance and input sensitivity between the unmanned aerial vehicles; andcombine the synchronized and calibrated audio streams to generate the three- dimensional sound stage of the location.

76. The computer-implemented process of claim 75, wherein the combining the synchronized and calibrated audio streams comprises: determining a time delay of arrival between pairs of audio streams from the plurality of unmanned aerial vehicles; and estimating a direction of arrival for sounds captured in the audio streams based on the determined time delays.

77. The computer-implemented process of claim 76, further comprising solving a set of distance equations based on the determined time delays and known locations of the unmanned aerial vehicles to determine a spatial position of a sound source within the three- dimensional sound stage.

78. The computer-implemented process of claim 75, further comprising applying beamforming techniques to the combined audio streams to enhance signals from specific directions and suppress noise from other directions within the three-dimensional sound stage.

79. The computer-implemented process of claim 75, further comprising generating, based on the three-dimensional sound stage and the three-dimensional volumetric model of space around the location, an immersive audio-visual representation of the location.

80. The computer-implemented process of claim 79, further comprising receiving a selected viewpoint within the three-dimensional volumetric model, and rendering audio corresponding to the selected viewpoint based on the three-dimensional sound stage.

81. The computer-implemented process of claim 73, further comprising initiating the remote control of the flight in response to an event occurring at the location.

82. The computer-implemented process of claim 81, further comprising selecting a plurality of the unmanned aerial vehicles based on the location of the event.

83. The computer-implemented process of claim 82, further comprising, in response to a selected point and camera angle within the volumetric model, generating image data corresponding to the selected point and camera angle from the combined streaming media based on the three-dimensional volumetric model.

84. The computer-implemented process of claim 83, further comprising exporting the three- dimensional volumetric model to a three-dimensional gaming platform.

85. The computer-implemented process of claim 73, further comprising generating a geofence defining an allowable flight zone for the unmanned aerial vehicle for the remote operation.

86. The computer-implemented process of claim 85, wherein the generating the geofence comprises: based on the geospatial location of launch of the registered unmanned aerial vehicle and a specified range for communication between the unmanned aerial vehicle and a remote control for the unmanned aerial vehicle, specifying the geofence based on a cylindrical space centered at the geospatial location with a radius based on the specified range and with a maximum flight altitude.

87. The computer-implemented process of claim 86, wherein the generating the geofence comprises applying a viewshed analysis within the cylindrical space to determine a minimum flight altitude to further constrain the geofence by the minimum flight altitude.

88. The computer-implemented process of claim 87, wherein the generating the geofence comprises applying data indicative of restricted airspace to the geofence to further constrain the geofence based on the restricted airspace.

89. The computer-implemented process of claim 88, wherein the generating the geofence comprises constraining the geofence based on strength of radio signals as received from the unmanned aerial vehicle.

90. The computer-implemented process of claim 85, further comprising, during performance of the remote operation, ensuring the unmanned aerial vehicle remains within the allowable flight zone.

91. A computer-implemented process for supporting use of remote-controlled unmanned aerial vehicles to a location within an allowable flight space, the computer-implemented process comprising: based on a geospatial location of launch of an unmanned aerial vehicle and a specified range for communication between the unmanned aerial vehicle and a remote control for the unmanned aerial vehicle, specifying a volume centered at the geospatial location and a shape based on the specified range and with a maximum flight altitude; defining the geofence based on an intersection between the volume and an excluded region; and remotely controlling flight of the unmanned aerial vehicle to the location while ensuring the unmanned aerial vehicle remains with the geofence.

92. The computer-implemented process of claim 91, further comprising applying a viewshed analysis within the volume to determine a minimum flight altitude to further constrain the geofence by the minimum flight altitude.

93. The computer-implemented process of claim 92, wherein the excluded region is specified in part by data indicative of restricted airspace.

94. The computer-implemented process of claim 93, wherein specifying the geofence comprises constraining the geofence based on strength of radio signals as received from the unmanned aerial vehicle.

95. A computer-implemented process for supporting use of a heterogenous supply of remote controlled unmanned aerial vehicles for remote operations by end users using a heterogeneous set of end user computing devices, the computer-implemented process comprising:in response to a request to register an unmanned aerial vehicle, receiving and storing data describing a geospatial location of launch and capabilities of the unmanned aerial vehicle; in response to a request from an end user computing device to perform a remote operation at a location using an unmanned aerial vehicle, selecting a registered unmanned aerial vehicle for the remote operation based on the location and parameters of the remote operation; transmitting, over a radio network, command and control data from the end user computing device to the unmanned aerial vehicle selected for the remote operation; and transmitting, over the wide-area network, status and streaming media data from the unmanned aerial vehicle selected for the remote operation to the end user computing device.

96. The computer-implemented process of claim 95, performed by cloud service which acts as a digital marketplace, facilitating commerce between service providers with a supply of remote and underutilized UAVs and end-users seeking UAV services.

97. The computer-implemented process of claim 96, further comprising: in response to an alert about an event at a location, selecting one or more unmanned aerial vehicles based on the location; remotely controlling flight of the selected one or more unmanned aerial vehicles to the location; directing the selected one or more unmanned aerial vehicles to capture and transmit live streaming media from the location over the radio network; and receiving and storing the streaming media data from the selected one or more unmanned aerial vehicles in computer storage as a source of truth of events occurring at the location.

98. The computer-implemented process of claim 97, further comprising: remotely controlling flight of one or more unmanned aerial vehicles to the location; directing the one or more unmanned aerial vehicles to capture and transmit live streaming media from the location; receiving and storing in computer storage streaming media data of a plurality of perspectives of the location from the one or more unmanned aerial vehicles; andcombining the streaming media from the plurality of perspectives from the one or more unmanned aerial vehicles into a three-dimensional volumetric model of space around the location.

99. The computer-implemented process of claim 98, further comprising generating a geofence defining an allowable flight zone for the unmanned aerial vehicle for the remote operation.

100. The computer-implemented process of claim 99, wherein generating the geofence comprises: based on the geospatial location of launch of the registered unmanned aerial vehicle and a specified range for communication between the unmanned aerial vehicle and a remote control for the unmanned aerial vehicle, specifying the geofence based on a volume centered at the geospatial location and size based on the specified range.

101. The computer-implemented process of claim 100, wherein generating the geofence comprises applying a viewshed analysis within the volume to determine a minimum flight altitude to further constrain the geofence by the minimum flight altitude.

102. The computer-implemented process of claim 101, wherein generating the geofence comprises applying data indicative of restricted airspace to the geofence to further constrain the geofence based on the restricted airspace.

103. The computer-implemented process of claim 102, wherein generating the geofence further comprises constraining the geofence based on strength of radio signals as received from the unmanned aerial vehicle.

104. The computer-implemented process of claim 103, further comprising, during performance of the remote operation, ensuring the unmanned aerial vehicle remains within the allowable flight zone.

105. The computer-implemented process of claim 95, further comprising, in response to a request to register an unmanned aerial vehicle, authenticating the unmanned aerial vehicle with the system.

106. The computer-implemented process of claim 105, wherein authenticating the unmanned aerial vehicle comprises receiving an authentication token from an authenticated drone operator, the authentication token vouching for the legitimacy of the unmanned aerial vehicle.

107. The computer-implemented process of claim 105, wherein authenticating the unmanned aerial vehicle comprises receiving a certificate from the unmanned aerial vehicle, the certificate signed by a certificate authority, and validating the certificate.

108. The computer-implemented process of claim 95, further comprising, during operation of the unmanned aerial vehicle, establishing a secure connection with the unmanned aerial vehicle, receiving a shared secret from the unmanned aerial vehicle with each request over the secure connection, and confirming the shared secret against an expected value.

109. The computer-implemented process of claim 95, further comprising receiving messages from the unmanned aerial vehicle signed with a private key of the unmanned aerial vehicle and validating the signed messages using a public key of the unmanned aerial vehicle.

110. The computer-implemented process of claim 95, further comprising, during operation of the unmanned aerial vehicle, including the authentication information with all subsequent communications from the unmanned aerial vehicle and validating the authentication information with each communication.

111. A computer-implemented process for supporting use of remote controlled unmanned aerial vehicles by multiple users, using a computer system including a cloud service, comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network and including a radio for communication data over a radio network to one or more unmanned aerial vehicles, the data including at least command and control data to the one or more unmanned aerial vehicles andstatus and streaming media data from the one or more unmanned aerial vehicles, the computer-implemented process performed by the cloud service comprising: receiving, from a user computer, an indication of a selected point within an image displayed in a graphical user interface of the user computer; determining a direction with respect to the drone based on the selected point; and controlling operation of the drone based on the determined direction.

112. The computer-implemented process of claim 111, wherein determining the direction comprises determining a geographic location in an environment of the drone.

113. The computer-implemented process of claim 111, wherein the operation comprises changing an orientation of a camera to direct a lens of the camera in the determined direction.

114. The computer-implemented process of claim 111, wherein the operation comprises moving the drone in the determined direction.

115. A computer-implemented process for supporting use of remote controlled unmanned aerial vehicles by multiple users, for use in a cloud service, comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network and including a radio for communication data over a radio network to one or more unmanned aerial vehicles, the data including at least command and control data to the one or more unmanned aerial vehicles and status and streaming media data from the one or more unmanned aerial vehicles, the computer-implemented process performed by the cloud service comprising: for each user, tracking activities performed through the user using one of the unmanned aerial vehicles; and comparing the tracked activities of a first user to tracked activities of a second user or to a threshold for tracked activities to output a comparison result.

116. The computer-implemented process of claim 115, further comprising associating reward data with the user based on the comparison result.

117. The computer-implemented process of claim 116, wherein the reward data comprises an amount of cryptocurrency tokens.

118. A computer-implemented process for supporting use of remote controlled unmanned aerial vehicles by multiple users, for use in a cloud service, comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network and including a radio for communication data over a radio network to one or more unmanned aerial vehicles, the data including at least command and control data to the one or more unmanned aerial vehicles and status and streaming media data from the one or more unmanned aerial vehicles, and a cryptocurrency system supporting cryptocurrency tokens connected to the wide-area network, the computer-implemented process performed by the cloud service comprising; monitoring interactions of users with the cloud service; and transferring cryptocurrency tokens to users based on the monitored interaction of the users with the cloud service.

119. A computer system for supporting use of remote controlled unmanned aerial vehicles by multiple users using a cloud service which includes at least one server computer connected to a wide-area network, at least one network appliance connected to the wide-area network and including a radio for communicating UAV data over a radio network to one or more unmanned aerial vehicles, the UAV data including at least command and control data to the one or more unmanned aerial vehicles and status and streaming media data from the one or more unmanned aerial vehicles, and at least one mobile device appliance connected to the wide-area network and including a radio for communicating mobile data over a communication network to one or more mobile devices, the mobile data including at least voice or text data to the one or more mobile devices and video or audio or text data from the one or more mobile devices, the computer-implemented process, performed by the cloud service, comprising: transitioning a user between communication with the one or more unmanned aerial vehicles and the one or more mobile devices; and managing transactions associated with the one or more mobile devices.

120. The computer-implemented process of claim 119, wherein managing transactions comprises transferring cryptocurrency tokens.

121. A computer-implemented process for supporting use of remote controlled unmanned aerial vehicles by multiple users using a cloud service, comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network and including a radio for communication data over a radio network to one or more unmanned aerial vehicles, the data including at least command and control data to the one or more unmanned aerial vehicles and status and streaming media data from the one or more unmanned aerial vehicles, the computer-implemented process performed by the cloud service comprising: processing the streaming media data from the unmanned aerial vehicles to identify organisms; and storing data about identified organisms in a database.

122. The computer-implemented process of claim 121, wherein processing the streaming media data comprises determining features describing appearance and characteristics of the organism from visual data in video frames of the streaming media.

123. The computer-implemented process of claim 122, wherein determining the features comprises using neural radiance fields.

124. The computer-implemented process of claim 123, wherein processing the streaming media data comprises classifying the organism with a classifier.

125. The computer-implemented process of claim 124, wherein the classifier comprises a trained classifier having neural radiance field data as an input.

126. The computer-implemented process of claim 125, further comprising performing a comparison of the determined features with features of registered organisms in the database.

127. The computer-implemented process of claim 126, further comprising creating a new record or updating an existing record in the database based on the comparison.

128. The computer-implemented process of claim 127, wherein the record includes species information.

129. The computer-implemented process of claim 128, wherein the record further includes at least one of the determined features, GPS coordinates, date, time, or contributor information.

130. A computer-implemented process for dynamically pricing insurance for unmanned aerial vehicle (UAV) operations using a cloud service, comprising at least one server computer connected to a wide-area network and at least one network appliance connected to the wide-area network and including a radio for communication data over a radio network to one or more unmanned aerial vehicles, the data including at least command and control data to the one or more unmanned aerial vehicles and status and streaming media data from the one or more unmanned aerial vehicles, the computer-implemented process, performed by the cloud service, comprising: receiving, from an end user computing device, a request for insurance coverage for a UAV operation at a specified location; obtaining, in real-time, data describing multiple risk factors associated with the specified location, the risk factors including at least location-specific hazards, time-of-day conditions, local airspace restrictions, current weather conditions, and historical incident data for the specified location; and analyzing the obtained real-time risk factor data to generate a risk profile specific to the requested UAV operation and, based on the generated risk profile, an insurance premium for the requested UAV operation.

131. The computer-implemented process of claim 130, further comprising transmitting data indicating the insurance premium to the end user computing device and, in response to receiving acceptance of the insurance premium from the end user computing device, activating insurance coverage for the UAV operation.

132. The computer-implemented process of claim 131, further comprising, during execution of the UAV operation, receiving real-time flight data from the UAV and analyzing the real-time flight data to dynamically update the risk profile and adjust the insurance premium based on the updated risk profile.

133. The computer-implemented process of claim 132, further comprising, during execution of the UAV operation, maintaining a record of the UAV operation, including the real-time flight data and any incidents.

134. The computer-implemented process of claim 133, further comprising accessing the maintained records of UAV operations, including the real-time flight data and any incidents, for use in the analysis to generate risk profiles.

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