57 results about "Aerial imagery" patented technology

The invention discloses a method for generating an aviation lifelike tree sample and identifying a tree species by using an image diffusion model. The method comprises the following steps: constructing a multi-modal data set; training an image-text comparison model and a Unet denoising network model; outputting predicted noise in the trained Unet denoising network model, and finally generating final image reconstruction which is in semantic alignment with the input text description; the obtained single tree crown images are synthesized; and constructing a YOLOv11 detection model, and training the YOLOv11 detection model by using the synthesized forest image to realize tree species identification. According to the method, language semantics and a diffusion model are combined to generate a vivid tree sample for capturing specific features of species and seasonal phenological changes, and the vivid tree sample is synthesized into a high-fidelity forest image, so that tree species identification in an aerial image is enhanced, and effective crown detection and accurate tree species identification are realized.

Described are systems and techniques for visualization and manipulation of non- composite oblique aerial imagery. Information can be obtained for an annotation feature for overlay on a displayed oblique image with an oblique viewpoint of a scene within a geographic area. Three-dimensional (3D) model data can be obtained for a portion of the geographic area and including 3D information of object surfaces within the scene. A 3D representation of the annotation feature can be generated using the 3D model data and georeferenced coordinates determined for the annotation feature. An oblique projection of the 3D representation of the annotation feature into the oblique viewpoint of the scene can be generated to include portions of the annotation feature unobstructed by the object surfaces within the scene. The oblique projection of the annotation feature can be output as a two-dimensional (2D) overlay on the displayed oblique image. Unlocking insights from Geo-Data, the present invention further relates to improvements in sustainability and environmental developments: together we create a safe and liveable world.

A method for farmland path recognition and planning based on aerial imagery is proposed. First, a data set (DOM) is constructed, cropped, and scale-normalized to form a dataset for recognition and planning. Next, multi-scale identification of the inter-row crop passage area is performed to obtain passable areas. Then, path planning is performed based on the inter-row structure: the centerline of the passable area is extracted to obtain a skeleton; principal direction constraints and structural consistency analysis are applied to the skeleton; branch removal and break repair are performed to obtain continuous, directional, and consistent inter-row centerline paths. The paths are clustered, grouped, and sorted to automatically distinguish different ridges and determine the walking order within the plot. Based on the endpoint relationships of adjacent inter-row paths, cross-row connection paths are generated at the ridge ends, forming a global navigation path combining straight and turning routes, and determining the entrance and exit points. Finally, the results are converted into standard path data for output, providing navigation for ground-based equipment. This invention effectively improves the stability and consistency of planning.

A platform for automatically detecting and analyzing defects in infrastructure assets uses aerial imagery, machine learning models and an interactive geographic information system (GIS) interface. Drone or aircraft images are processed by deep neural networks to identify and quantify surface and subsurface defects. Defects are mapped onto 3D models with precise geospatial coordinates. A web platform allows engineers to visualize defects from individual or multiple time periods, filter and query based on attributes, analyze density heatmaps, highlight changes, and perform side-by-side comparative analysis. The unique interface enables rapid condition assessments to support proactive maintenance.

An improved living area estimation system is described herein that can obtain aerial images of structures and property data corresponding to structures. The improved living area estimation system can then perform image processing on the aerial images to estimate a living area of each structure. The improved living area estimation system can group property records for the various structures based on one or more criterion and, for each group of property records, determine the average difference between living area calculated using aerial images and living area included in the property data. For a particular structure, the improved living area estimation system can process the aerial image(s) to estimate living area and then modify this value by the average difference determined for the group to which the structure belongs to determine an estimated living area of the structure.

The invention provides a multispectral target detection method based on prompt learning. The multispectral target detection method is characterized by comprising the following innovative steps: step 1, preprocessing aerial image data; and step 2, combining a deep learning model with a prompt learning model Global-Aware Prompt Block (GAP-Block) provided by the invention to extract image features. And step 3, fusing the features of the multi-modal image by using an adaptive Multi-Feature Fusion (AMFF) method provided by the invention. And a fourth step of enhancing the image features by using a feature pyramid network (FPN). And 5, inputting the image features into a target detector for detection. The method is combined with prompt learning to solve the problems that the training cost is high and current scale features among different modalities are mainly concerned when an existing method is used for downstream task training, multi-modal feature graphs can be dynamically integrated, and the feature expression ability is further improved; and an efficient and flexible method is provided for a multi-modal target detection task.

The invention discloses an unmanned aerial vehicle aerial image-based wetland waterfowl automatic identification and statistics method and system, and belongs to the technical field of software. The objective of the invention is to solve the problem of inaccurate population scale and spatial distribution statistical results caused by missed detection, false detection and uncertainty of an unmanned aerial vehicle aerial photographing waterfowl recognition result in the prior art. Therefore, the invention provides a method which comprises the following steps of: dividing a grid and constructing a multi-frame observation effort tensor; performing waterfowl detection and trajectory aggregation based on multi-frame images; modeling based on the observation effort, the scene features and the detection probability of manual checking; performing layered inference on the real number of grids based on the detection model; fusing real intensity field inference of spatial adjacency and ecological factors; and updating the time sequence state space under multi-period monitoring. Therefore, statistical inference of the real number and spatial distribution of the waterfowls is realized, and the method has the advantages of high statistical reliability and accurate uncertainty quantification.

The invention relates to a ground station system (38a-c) for georeferencing aerial images, in particular for calibrating and / or surveying drone photogrammetry data, of an area (10a-c), such as, for example, a construction site, an archaeological find and / or excavation site or an infrastructure facility, with a plurality of control point units (12a-c, 14a-c, 16a-c) that can be arranged distributed over the area (10a-c), wherein each of the control point units (12a-c, 14a-c, 16a-c) has an optical marker (18a-c) on at least one surface that is uniquely identifiable from the air. It is proposed that each of the control point units (12a-c, 14a-c, 16a-c) is associated with a Global Navigation Satellite System (GNSS) module (20) for acquiring its instantaneous geographic position and a Wide Area Network (WWAN) module (28a-c) for at least wirelessly transmitting the acquired instantaneous geographic position, wherein at least one of the control point units (12a-c, 14a-c, 16a-c) is integrated into a functional object (22a-c, 24a-c, 26a-c) that is at least temporarily assigned to the area (10a-c) and performs a main function within the area (10a-c) that is different from georeferencing.

An autonomous system and method for capturing event-driven aerial imagery utilizes a drone equipped with advanced sensors and navigation subsystems to operate without human intervention. Upon detecting predefined target events, such as structure fires, explosions, gunshots, emergency sirens, a specific license place, or a specific face, the drone autonomously launches and employs direction-finding triangulation to pinpoint the event's latitude, longitude, and elevation. The drone autonomously executes optimized flight profiles. Data transmission utilizes bonded and blended communication channels to ensure reliable video streaming to users, such as first responders or news agencies. Compliance with FAA altitude regulations is enforced, and the system can be controlled remotely via internet or cellular connections. Continuous operation is enabled through tethered power or automated battery replacement stations. Applications include law enforcement, emergency services, and news gathering, providing immediate aerial reconnaissance without requiring human operators to be present at unpredictable event locations.

An autonomous system and method for capturing event-driven aerial imagery utilizes a drone equipped with advanced sensors and navigation subsystems to operate without human intervention. Upon detecting predefined target events, such as structure fires, explosions, gunshots, emergency sirens, a specific license place, or a specific face, the drone autonomously launches and employs direction-finding triangulation to pinpoint the event's latitude, longitude, and elevation. The drone autonomously executes optimized flight profiles. Data transmission utilizes bonded and blended communication channels to ensure reliable video streaming to users, such as first responders or news agencies. Compliance with FAA altitude regulations is enforced, and the system can be controlled remotely via internet or cellular connections. Continuous operation is enabled through tethered power or automated battery replacement stations. Applications include law enforcement, emergency services, and news gathering, providing immediate aerial reconnaissance without requiring human operators to be present at unpredictable event locations.

Described are systems and techniques for visualization and manipulation of non-composite oblique aerial imagery. Information can be obtained for an annotation feature for overlay on a displayed oblique image with an oblique viewpoint of a scene within a geographic area. Three-dimensional (3D) model data can be obtained for a portion of the geographic area and including 3D information of object surfaces within the scene. A 3D representation of the annotation feature can be generated using the 3D model data and georeferenced coordinates determined for the annotation feature. An oblique projection of the 3D representation of the annotation feature into the oblique viewpoint of the scene can be generated to include portions of the annotation feature unobstructed by the object surfaces within the scene. The oblique projection of the annotation feature can be output as a two-dimensional (2D) overlay on the displayed oblique image.

This invention belongs to the field of UAV autonomous positioning and navigation technology, and relates to a UAV map construction method for environments where satellite imagery is unavailable. Specifically, it provides a method for constructing a UAV work area map based on aerial imagery, addressing issues such as image distortion and edge misalignment in existing area map construction methods. This invention orthogonally divides the UAV's aerial photography work area into a grid according to the same geographic coordinate system. The UAV performs a coverage flight mission within the work area according to a planned flight path, maintaining an interval of 50% of the field of view width between adjacent flight paths. During aerial photography, the UAV simultaneously acquires image and geographic positioning information (GPS information and yaw angle information of the center point) as image location points. Based on UAV sensor data and the UAV's flight trajectory and pose, keyframes of geographic location information are extracted from the aerial video, and then used to fill the corresponding grid content to obtain a global map of the work area.

This invention provides a method for generating 3D objects from images of buildings taken from the air using an aerial videography device. [Solution] The method includes a calculation unit that generates an initial spatial object which is a 3D object corresponding to a building based on multiple aerial images, and a mapping candidate selection step, defining an object surface corresponding to the exterior surface of the building and a building surface normal vector perpendicular to the exterior surface of the building on the building surface. At least one of the multiple aerial images includes an initial texture corresponding to the exterior surface of the building, and a shooting direction vector from the shooting device toward the exterior surface of the building is defined for each of the multiple aerial images. The method also includes an intermediate texture selection step in which the calculation unit selects an aerial image for which the dot product of the shooting direction vector and the building surface normal vector is negative as a mapping candidate aerial image, and selects the initial texture with the largest area from among multiple initial textures corresponding to the multiple mapping candidate aerial images as an intermediate texture.

The present application belongs to the technical field of geological prospecting and evaluation, and discloses a kind of ore body positioning method and rapid sampling device for niobium-tantalum ore target area prediction and evaluation.The present application is based on existing geological data, through the analysis of various geological bodies in 1:250,000-1:10,000 geological map and the identification of the distribution, shape, intrusive contact relationship and formation age of alkaline rock mass, the favorable metallogenic area is delineated;Through satellite and aerial image identification of linear structure, ring structure and alteration information, target area positioning is assisted;Through magnetic exploration, ore-bearing rock mass and fault structure are detected, and potential position of ore body is delineated, combined with geological data, geological properties of anomaly source are inferred;Using radioactive detection equipment, the total amount of gamma at different positions in the niobium-tantalum prediction target area is measured, and the position of niobium-tantalum ore is preliminarily judged according to the gamma anomaly result;Using rapid sampling device, target area is systematically sampled and analyzed.Through the method and device, metallogenic geological conditions and prospecting potential can be evaluated, and exploration grid is optimized.

Described are systems and techniques for visualization and manipulation of non- composite oblique aerial imagery. One or more user inputs can be obtained by an oblique image visualization system associated with a plurality of non-composite oblique aerial images of a geographic area of interest. View perspective information indicative of a user-requested view of a scene or area within the geographic area of interest can be determined from the user inputs. One or more parameters of the view perspective information can be compared to a corresponding one or more parameters associated with each oblique image. A best match oblique image can be selected from the plurality of non-composite oblique aerial images based on the comparison. The best match oblique image can be output for display by the oblique image visualization system in response to the user-requested view. Unlocking insights from Geo-Data, the present invention further relates to improvements in sustainability and environmental developments: together we create a safe and liveable world.

This invention relates to the field of architectural surveying technology and discloses an architectural surveying method based on aerial photography. The system includes: generating an initial building outline based on a building image dataset; determining the initial shooting interval of a camera based on basic dimension data; acquiring aerial image data of the building to be surveyed; performing 3D reconstruction of the initial building outline based on the aerial image data to generate an initial 3D point cloud model of the building; determining whether to adjust the initial shooting interval based on local density; when it is determined that the initial shooting interval should be adjusted, collecting coverage data of the building to be surveyed, calculating a shooting influence factor based on the coverage data and local density; comparing the shooting influence factor with historical data, adjusting the initial shooting interval based on the comparison results; and storing the shooting influence factor. This invention not only improves the efficiency of architectural surveying but also enhances the quality of surveying results, providing strong technical support for urban planning and construction.

The invention discloses a method for screening influence factors of a forest fire early warning model, and the method comprises the following steps: 1, collecting forest related information, and importing a forest position in the forest related information into an Internet database; 2, extracting a forest plan from the Internet database, and processing the forest plan to obtain a first influence factor; 3, historical fire information of the forest is extracted from the forest related information, the historical fire information is processed, and a second influence factor is obtained; 4, acquiring weather information of the position of the forest from the interconnected database, and processing the weather information to obtain a third influence factor; 5, the forest related information further comprises low-altitude aerial image information of the forest, the low-altitude aerial image information of the forest is processed, and a fourth influence factor is obtained. According to the method, the influence factors of the forest fire early warning model can be better screened.

This invention belongs to the field of smart agriculture technology and collaborative operation of unmanned systems. It discloses a method and system for mobile intelligent agricultural unmanned swarm operations. The method includes: generating a global verification path that minimizes the sum of the total travel distance and the soil compaction penalty; generating a soil property distribution map and fusing aerial imagery data to obtain a farmland fusion perception map; rasterizing the farmland areas in the fusion perception map, treating each abnormal grid as a task, and assigning bidding weights to agents for tasks based on the suitability of the agents in performing the corresponding tasks; allocating agent tasks with the objective of maximizing the total sum of global bidding weights; and predicting the trajectory of the aerial operation drones using a discrete linear model, with each aerial operation drone solving the optimization problem locally within a distributed model predictive control framework. This invention improves operational efficiency and accuracy.

To provide an improved method for operating an aerial image measurement system and / or an aerial image measurement system. [Solution] A method for operating an airborne image measuring system (100) for qualification and / or measurement of an EUV mask (102), the airborne image measuring system comprising a vacuum chamber (104) having a measurement process chamber (106) and a process preparation chamber (108), an EUV plasma source (110), and a vacuum lock (112) for transferring the EUV mask into the process preparation chamber, the method comprising: performing preparatory measures in parallel in and / or in or within the vacuum chamber to prepare for qualification and / or measurement of the EUV mask; and qualifying and / or measuring the EUV mask by the airborne image measuring system by capturing an airborne image of the EUV mask.

A system and method for viewing aerial imagery and derived content on a web-based imagery browser that can be accessed by at least two users simultaneously. In addition, a system for interactively generating 3D geometry data for a location imaged from multiple viewpoints.

Methods, non-transitory computer-readable storage media, and computer or computer systems directed to detecting, analyzing, and tracking roads and grading activity using satellite or aerial imagery in combination with a machine learned model are described.

The invention provides a multi-view aerial image depth map generation and earth surface three-dimensional reconstruction method, and relates to the technical field of building three-dimensional reconstruction. The method comprises the following steps: acquiring a multi-view aerial image; reconstructing the multi-view aerial image through a multi-branch edge enhancement network to obtain an edge-enhanced high-resolution image; performing depth estimation on the edge-enhanced high-resolution image to generate a depth map; depth value filtering is carried out on the depth map based on luminosity and geometric consistency, and a three-dimensional dense point cloud is formed through three-dimensional space projection fusion; and reconstructing the three-dimensional dense point cloud data by adopting a Poisson reconstruction algorithm to generate a grid model. According to the multi-view aerial image depth map generation and earth surface three-dimensional reconstruction method, manual intervention and dependence on three-dimensional laser point cloud data are reduced, and automatic and efficient processing of building three-dimensional reconstruction is achieved.

The invention discloses a method and a device for realizing geological capping based on an airborne flight image and a real earth surface. The method comprises the following steps: determining the range of a flight image research area according to the position of an earth exploration survey line, identifying stratum distribution and fracture in the range, and determining the tendency and dip angle of the stratum by combining a digital elevation model of the earth surface; drawing a measuring line surface elevation curve based on the digital elevation model, and according to the geological element interpretation result and the digital elevation model, by taking the surface elevation curve as a top boundary, sequentially drawing a stratum line, marking fractures, extending the stratum line downwards according to degrees of an inclination direction and an inclination angle, and generating a superficial geological structure section of the measuring line; and geological capping is carried out by utilizing a superficial geological structure section. According to the method, a high-precision airborne flight image and a digital elevation model are adopted, geological information of an earth exploration survey line range is interpreted, a survey line geological structure section is generated, geological capping of an underground section is achieved, superficial missing information is made up, and processing and interpretation of earth exploration data are assisted.

The present application relates to the field of photogrammetry, and discloses a land space image analysis method based on photogrammetry, comprising: acquiring aerial images and attitude data of a survey area to generate sparse point clouds; distinguishing rigid and non-rigid feature points based on semantic segmentation results; establishing a local vertical reference system for non-rigid points and configuring anisotropic parameters with transverse variance greater than longitudinal variance; mapping the parameters to a global weight matrix using a coordinate rotation matrix, and substituting them into a bundle adjustment model to complete iterative solution, the present application effectively realizes the automatic suppression of non-rigid disturbance of vegetation and water area by constructing an anisotropic weight determination mechanism coupled with physical properties and geometric constraints, and improves the geometric accuracy and stability of three-dimensional reconstruction in complex land space scenes.

An aerial image-based object detection method using of a neural network model comprising an input size, the method includes: taking a 2D image (1), dividing the taken 2D image (1) into patches (2) of size equal to the input size of the neural network model, saving the coordinates of the same reference point for each of the patches (2), resizing the taken 2D image (1) to the input size of the neural network model, saving the coordinate of a reference point of the resized taken 2D image (1) and the scale of the resize, stacking into a batch (3) the patches (2) and the resize of the taken 2D image (1), passing the batch (3) to the neural network model to determine object detections, and transforming the local detections to a reunified image (4) by using the saved patch coordinates.

A method for operating an aerial image measuring system for the qualification and / or measurement of an EUV mask, the aerial image measuring system comprising a vacuum chamber with a measuring process chamber and a process preparation chamber, an EUV plasma source, and a vacuum lock for transferring the EUV mask into the process preparation chamber, the method comprising parallel execution of preparation measures within the vacuum chamber and / or at or in the vacuum lock in order to prepare the qualification and / or measurement of the EUV mask; and qualifying and / or measuring the EUV mask by use of the aerial image measuring system by capturing an aerial image of the EUV mask.

A computing system comprises one or more processors and one or more storage devices that comprise instruction code that is executable by the one or more processors. The instruction code is executable by the processors to cause the computing system to receive an aerial image that depicts a property. Input embeddings associated with the aerial image are generated by a vision transformer. The vision transformer transforms the input embeddings to output embeddings that specify features of the aerial image. The vision transformer is trained using a self-supervised learning technique to generate the output embeddings. The ViT communicates the output embedding to a loss model. The loss model is trained to predict a loss score associated with the property depicted in the aerial image. An indication of the loss score associated with the property is output by the computing system.

This invention relates to the field of river management technology, specifically to a river monitoring system and method based on UAV aerial imagery, comprising: an upload module for uploading river distribution parameters and constructing a river distribution model based on these parameters; a selection module for traversing the river distribution model and selecting aerial photography points within it; and a sensing module for real-time sensing of solar pose information. In the process of acquiring river images based on aerial photography points, this invention ensures that the aerial photography point, the location information of the aerial photography equipment itself, and the position coordinates of the sun relative to the UAV aerial photography equipment are collinear. This guarantees, to the greatest extent possible, that there is no light reflection in the river images during the acquisition process, resulting in more reliable evaluation results when the acquired river images are used for river health assessment.

This invention proposes a point-supervised rotating target detection method based on placement priors. The method includes: dividing the original aerial imagery into a training set and a test set for directional target detection; obtaining pixel-level feature maps using the aerial imagery in the training set; obtaining directional bounding boxes corresponding to each pixel based on the pixel-level feature maps; constructing angle prior loss, size prior loss, and Voronoi watershed rotating bounding box loss based on a set of positive sample rotating bounding boxes, thereby constructing a training objective function; optimizing the target detection baseline model through backpropagation based on the training objective function, obtaining an optimized target detection baseline model; and inputting the test set into the optimized target detection baseline model to obtain the target detection results. This invention requires only single-point annotation to train an accurate rotating target detection model, significantly reducing annotation time and manpower costs compared to rotating bounding box annotation, and is suitable for large-scale dataset construction and rapid deployment scenarios.

The invention discloses an air-ground image alignment method. The method comprises the following steps: carrying out aerial triangulation on an aerial image set and a ground image set; based on the obtained aerial triangulation result, key images are extracted from the aerial image set and the ground image set; inputting the key image into a deep learning model, outputting an aviation relative depth map and a ground relative depth map, and converting the aviation relative depth map and the ground relative depth map into an aviation absolute depth map and a ground absolute depth map; based on the absolute depth map, aviation dense point clouds and ground dense point clouds are generated, and aviation virtual plane primitives and ground virtual plane primitives are extracted from the aviation dense point clouds and the ground dense point clouds; extracting texture features and geometric features of the aviation virtual plane primitives and the ground virtual plane primitives, and screening out a preset number of plane primitive pairs; calculating a conversion parameter based on the plane primitive pair, and converting the aviation point cloud to a ground coordinate system according to the conversion parameter so as to realize alignment of the aviation image and the ground image; according to the invention, the precision and overall efficiency of air-ground image alignment can be significantly improved.