Threat assessment system

Abstract

The invention relates to a threat assessment system for assessing threats to civil aviation and airports. The system includes a platform that carries geographic information system hardware components for obtaining geospatial metadata of the airport and regions that surround it. A database management system for receiving, fusing, and managing the geospatial metadata, and attribute and / or statistical data of the geographical area and objects that locate therein is also provided. A threat forecasting server forms part of the system and provides graphic representations of a possible future threat to civil aviation and the airport. A computing device for interfacing with the platform, the database management system, and the threat forecasting server is provided. Geographic information system software is executable on the computing device and permits a police officer to analyze geospatial metadata, statistical and / or attribute data, and effects that the threat may have on civil aviation and the airport graphically.

Description

[0001] THREAT ASSESSMENT SYSTEM

[0002] FIELD OF THE INVENTION

[0003] The invention relates to a threat assessment system. More particularly, but not exclusively, the invention relates to a threat assessment system for assessing threats to civil aviation and airports.

[0004] BACKGROUND TO THE INVENTION

[0005] The task of monitoring and evaluating possible security threats to civil aviation and airports is complex, time-consuming, and dynamic. Security threats to civil aviation and airports include, but is not limited to, terrorist attacks, fires, natural disasters, crime, failures of critical infrastructure, and the like. Thousands of passengers enter and leave an airport each day, and the safety of these passengers must also be ensured.

[0006] Areas that surround an airport must also be monitored and evaluated for possible threats to civil aviation and the airport itself.

[0007] Contingency plans for emergencies must also be formulated by officers and agencies who are responsible for monitoring and evaluating possible threats to civil aviation and airports. An officer or agency must ideally have as much information as possible at their disposal to effectively monitor and evaluate possible security threats to civil aviation and airports. The appropriateness and effectiveness of a contingency plan for a specific security threat also depends heavily on the information used to formulate the contingency plan.

[0008] Therefore, firstly, information that may be relevant to possible security threats to civil aviation and airports must be obtained. Once the information has been obtained, it must be evaluated. After it has been evaluated, appropriate responses and / or actions, if necessary, can be recommended and / or taken.

[0009] Conventionally, officers and agencies that are responsible for monitoring and evaluating possible security threats to civil aviation and airports must use disjointed databases and information sources to monitor and evaluate possible security threats. This is a time-consuming and costly process as all the information must be cleaned, organized, and then evaluated. Once evaluated, the officer or agency may recommend actions that need to be taken to safeguard passengers, airport infrastructure, and the like. In an emergency, for example during a terrorist attack, there is simply not enough time to use all relevant information to evaluate the threat posed by the emergency and to recommend an appropriate response or action that must be taken.

[0010] From the above, it will be appreciated that there is a need for a system that can combine information from disjointed databases and information sources, organize the information, and assist an officer or agency with monitoring and evaluating possible security threats.

[0011] OBJECT OF THE INVENTION

[0012] It is an object of the present invention to provide a threat assessment system with which the applicant believes the above-mentioned disadvantages would at least partially be addressed or which would provide a useful alternative to known systems for assessing a threat.

[0013] SUMMARY OF THE INVENTION

[0014] According to present invention, there is provided a threat assessment system for retrieving, fusing, managing, processing, and analyzing geospatial metadata, together with attribute and / or statistical data, the threat assessment system comprising: a platform including: o an image capturing device for capturing images of a geographical area that surrounds the image capturing device; o a light detection and ranging (LIDAR) sensor for measuring a distance between the platform and objects and surfaces that surround the platform; o a geolocation device for identifying a location, and / or a state, and / or an orientation of the platform when an image is captured, and / or a distance is measured; o a timekeeping device for identifying and associating each image that has been captured and / or distance that has been measured with a date and time; o a memory module for storing geospatial metadata of the images that were captured, the distances that were measured, the geographic locations of the platform that were identified, the states of the platform that were identified, the orientations of the platform that were identified, and the dates and times that were identified; o a communication module for receiving data signals from a remote location and / or sending the geospatial metadata signals to a remote location over a communication network; and o a processor module that is configured to execute program logic for controlling the operation of the image capturing device, the light detection and ranging (LIDAR) sensor, the geolocation device, the timekeeping device, the memory module, and the communication module; a database management system that is configured to: o receive, fuse, and manage the geospatial metadata; o receive, fuse, and manage attribute and / or statistical data of the geographical area, the objects and surfaces that surround the platform , and / or historical data on events that occurred within or had a bearing on the geographical area of interest; and o store the fused datasets in a database; and a computing device including: o a memory module on which an operating system and geographic information system software has been loaded; o a display for displaying a graphical user interface of the geographic information system software; o a computing input device for controlling a functionality of the geographic information system software and selecting geospatial metadata and additional data from the database of the database management system that must be processed by the geographic information system software; o a communication module that is configured to establish a data connection with the database management system and the platform over the communication network; o a processor module that is configured to:

[0015] ■ retrieve the selected geospatial metadata and data from the database of the database management system ;

[0016] ■ retrieve the geographic information system software from the memory module; and

[0017] ■ execute the geographic information system software to retrieve, fuse, manage, and process the geospatial metadata together with the selected data from the database of the database management system, and provide output data for analyzing the geospatial metadata together with the data from the database, wherein the output data is displayed on the graphical user interface for analysis by a user of the system as virtual layers that have been superimposed onto one another, and wherein each virtual layer provides a graphic representation of data that have been processed through the geographic information system software. The platform may be stationary or mobile. A plurality of platforms may form part of the system. The platform may be a ground vehicle or an aerial vehicle. One example of an appropriate ground vehicle is a motor vehicle. One example of an appropriate aerial vehicle is an unmanned aerial vehicle. The ground or aerial vehicle may be controlled by a driver or pilot. The ground or aerial vehicle may also be autonomous vehicles.

[0018] A plurality of image capturing devices may be mounted on the platform. The image capturing device may be a digital camera. The digital camera may be configured to take photos and / or record videos of the geographical area that surrounds the platform. The digital camera may also be configured to take photos and / or record videos of objects and surfaces that surround the platform. The image capturing device may be an omnidirectional digital camera. Alternatively, the image capturing device may be a camera rig that supports six digital cameras. The six digital cameras may be arranged to take photos and / or record videos of the geographical area in all directions surrounding the camera rig.

[0019] The light detection and ranging (LIDAR) sensor may use a phase-shift method to measure a distance between the platform and objects and surfaces that surround the platform. That is, instead of using a pulsed laser source, a continuous source is used, and its power is modulated at a constant frequency. This means that the input can be looked at like a sine curve with time on the x-axis and laser power on the y-axis. Photodetectors of the LIDAR sensor detects laser beams that bounce off objects that are scanned. The photodetectors also measure the power of the laser beams that bounce off the objects. Thus, a sine curve of the return laser beam may be formed. By comparing the phase difference, the difference in radians of the peaks of the two sine waves, the distance to the object can be found with the below equation, where d is distance, c is the speed of light, A<P is the phase difference, and f is the frequency at which the power was modulated.

[0020] A plurality of geolocation devices may form part of the platform. Examples of geolocation devices that may form part of the platform include, but is not limited to, a global positioning system (GPS) receiver, an inertial measurement unit (IMU), a gyroscope, a compass, and an altimeter. Preferably, all the geolocation devices are digital devices that collect, store, and transmit data in a digital, computer-readable format.

[0021] The timekeeping device may be a digital clock. Alternatively, the timekeeping device may take the form of clock software that has been loaded onto the memory module of the platform and which is executable by the processor module of the platform.

[0022] The memory module of the platform may be a non-transient computer-readable medium. It is envisaged that the memory module of the platform may also be a data storage device, for example, an electro-mechanical data storage device, or a solid- state drive. The memory module may also include a non-transient computer-readable medium, as well as a data storage device. The communication module of the platform may be a wireless communication device that is configured to establish and maintain a data connection, over a communication network, between the wireless communication device and the database management system, and / or the wireless communication device and the computing device. The processor may control the operation of the wireless communication device. The processor may take the form of or include a central processing unit. The processor may be configured to instruct the wireless communication device to retrieve data from the memory module and to transmit said data to the database management system and / or the computing device. The processor may also be configured to execute program logic and / or instructions that are sent from the computing device, over the communication network, to the wireless communication device of the platform.

[0023] The program logic that must be executed by the processor of the platform may also be stored on and retrieved from the memory module of the platform. The program logic may comprise software that controls the platform and its constituent parts.

[0024] A user of the threat assessment system may establish a data connection between the computing device and the platform. The data connection may be established by sending a data signal from the communication module of the computing device, over the communication network, to the communication module of the platform. The user may then use the computing device and its input device to send instructions as digital signals to the platform and / or its constituent parts. More particularly, digital signals may be sent to the communication module of the platform and executed by the processor of the platform. Non-exhaustive examples of instructions that may be sent to the platform and / or its constituent parts include: instructions for controlling the movement of the platform to guide the platform to a specified location, or on a specified trajectory; instructions for controlling the operation of the image capturing device (e.g., the speed and / or resolution at which images must be captured) and / or light detection and ranging (LIDAR) sensor (e.g., the frequency at which distances must be measured); instructions for controlling the operation of the communication module (e.g., how often data signals should be sent from the platform to the database management system and / or the communication module of the computing device); and instructions for controlling the processor module of the platform.

[0025] The database management system may act as an interface between a database that forms part of the database management system, the platform, and / or the computing device. The database of the data management system may be updated continuously with data that were obtained in situ by the platform and its constituent parts. Thus, the database may be updated with real-time or near real-time data that is obtained by the platform and its constituent parts. Data that are sent to and received by the database management system may be stored as tables in a relational database. It is envisaged that the database management system may form part of the computing device.

[0026] The computing device may be a smart device. Non-exhaustive examples of appropriate smart devices include: a desktop computer; a laptop computer; a tablet; a server; and a cloud computing architecture.

[0027] It will be appreciated that the term “smart device” means an electronic device that is connectable to other devices, networks, and servers and that can operate interactively with the said other devices, networks, and servers.

[0028] The memory module of the computing device may be a data storage device. Non- exhaustive examples of appropriate data storage devices include: a non-transient computer-readable medium; an electro-mechanical data storage device; a solid-state drive; and combinations of the above devices.

[0029] The display of the computing device may be a digital screen.

[0030] Non-exhaustive examples of the computing input device include: a keyboard; a mouse; a touchscreen; a trackball; a microphone; and a controller. A plurality of computing input devices and / or combinations of the above-listed computing input devices may be used.

[0031] The communication module of the computing device may be configured to establish data connections with the platform and / or the database management system , over the communication network, via one or more ports using a network protocol. The ports may be physical or virtual ports. The communication module may also be a wireless communication device.

[0032] The processing unit of the computing device may take the form of or include a central processing unit. The processing unit may include a plurality of central processing units.

[0033] The geographic information system software may be an off the shelf geographic system software package.

[0034] The threat assessment system may also include a threat forecasting server that is configured to establish a data connection with the database management system and / or the computing device.

[0035] The threat forecasting server may be configured to provide identifiers of a possible future threat, the identifiers being graphic representations of attributes of the possible threat that are calculated and provided by a machine learning model that was obtained by processing type, date, time, and location identifiers of historical threats through a machine learning algorithm, the threat forecasting server comprising: a receiver module that is configured to receive input data from the computing device, the input data taking the form of a request for output data that pertain to a possible future threat that has been specified by a user of the threat assessment system; a memory storage device on which a machine learning model and the input data is stored; a processor module that is configured to retrieve the machine learning model and the input data from the memory storage device, and to process the input data through the machine learning model to provide extrapolated output data, the output data including at least type, time, location, and probability identifiers of the possible future threat; and a communication module that is configured to transmit the output data of the processor module, in a format that is compatible with the geographic information system software, to the computing device.

[0036] The machine learning model may be trained by a method including the steps of:

[0037] (i) compiling a database or databases of historical threats, the database or databases comprising at least type, date, time, and location identifiers of the historical threats;

[0038] (ii) retrieving the data (i.e., identifiers) from the database or databases;

[0039] (iii) refining the data to provide refined data;

[0040] (iv) summarizing the data to provide summarized data; and

[0041] (v) processing the summarized data through a machine learning algorithm to provide a machine learning model that is suitable for processing the input data, and extrapolating output data therefrom that correspond to a possible future threat. The machine learning algorithm may be a k-nearest neighbors algorithm, a k-means clustering algorithm, or a linear regression algorithm.

[0042] The threat forecasting server may form part of the computing device.

[0043] All data may be encoded before it is sent over the communication network. The data may also be encrypted before it is sent over the communication network and decrypted before it is used in the geographic information system software.

[0044] DETAILED DESCRIPTION OF THE INVENTION

[0045] The present invention is described below, with reference to a possible threat to civil aviation and / or an airport. However, it will be appreciated that the threat assessment system of the present invention can also be implemented and / or used for assessing a possible threat to any public and / or private space. For example, the threat assessment system of the present invention may be implemented and / or used to assess a possible threat to a financial district, a building, a sports complex, a harbor, a nature reserve, and the like.

[0046] As used herein, the term “and / or” means that one or more, or even all, of the cases it connects may occur. For example, the phrase “A and / or B and / or C” should be interpreted as:

[0047] A, B, or C;

[0048] A and B;

[0049] A and C; B and C; and

[0050] A, B, and C.

[0051] As used herein, the term “data connection” means any interface through which data are transmitted. The data may be transmitted via conventional wired or wireless communication systems.

[0052] The present invention pertains to a threat assessment system for retrieving, fusing, managing, processing, and analyzing geospatial metadata, together with attribute and / or statistical data. A threat forecasting server also forms part of the system and permits a user of the system to incorporate simulated threats into the system for analysis.

[0053] The threat assessment system includes a platform that takes the form of an autonomous unmanned aerial vehicle. The autonomous unmanned aerial vehicle carries a camera rig that supports six digital cameras. The digital cameras are arranged to capture red, green, and blue images and / or videos of a geographical area that, in flight, locate below the unmanned aerial vehicle.

[0054] The autonomous unmanned aerial vehicle also carries a light detection and ranging (LIDAR) sensor for measuring a distance between the unmanned aerial vehicle and objects and surfaces that, in flight locate below the unmanned aerial vehicle. The light detection and ranging (LIDAR) sensor may also be arranged to measure a distance between the unmanned aerial vehicle and an object which, in flight, is on the same horizontal plane as the unmanned aerial vehicle. For example, an outside surface of a high-rise building. The light detection and ranging (LIDAR) sensor is also configured to measure distances between the unmanned aerial vehicle and surfaces that locate between the object that is on the same horizontal plane as the unmanned aerial vehicle and the ground. For example, a face of a high-rise building.

[0055] The autonomous unmanned aerial vehicle carries a global positioning system (GPS) receiver for receiving global positioning system (GPS) signals from a plurality of global positioning system (GPS) satellites and identifying the location of the unmanned aerial vehicle relative to a geographic area that, in flight, locates below the unmanned aerial vehicle, and / or objects and / or surfaces that, in flight, locate below or on the same horizontal plane as the unmanned aerial vehicle. The global positioning system (GPS) receiver is configured to decode global positioning system (GPS) signals and / or issue location signals and / or data.

[0056] The autonomous aerial vehicle also carries an inertial measurement unit (IMU) that is arranged to measure and transmit the unmanned aerial vehicle’s, and its constituent parts’ (i.e. , other geolocating devices that are carried by unmanned aerial vehicle) specific force, angular rate, and orientation. Hereinafter, the afore is collectively referred to as “the state” of the unmanned aerial vehicle or its constituent parts. The inertial measurement unit (IMU) includes an accelerometer, gyroscope, and magnetometer. The inertial measurement unit (IMU) is coupled to the autonomous aerial vehicle and its constituent parts.

[0057] In addition to the above, the autonomous unmanned aerial vehicle also carries a digital clock that keeps precise date and time measurements that are used to synchronize events within the image capturing device and the light detection and ranging (LIDAR) sensor. It will be appreciated that, ultimately, the dates and times are used to synchronize events on a geographic information system software package.

[0058] The autonomous unmanned aerial vehicle is provided with a non-transient computer- readable medium that is configured to store geospatial metadata of the images that were captured, the distances that were measured, the geographic locations of the unmanned aerial vehicle that were identified, the states of the unmanned aerial vehicle and its constituent parts that were identified, the orientations of the unmanned aerial vehicle and its constituent parts that were identified, and the dates and times that were identified.

[0059] A wireless communication device is provided on the autonomous unmanned aerial vehicle. The wireless communication device is configured to establish and / or maintain a data connection, over a communication network, with a database management system and a computing device. Thus, data signals may be exchanged between the wireless communication device and the database management system and / or computing device. The communication network may be a private or public communication network.

[0060] The autonomous unmanned aerial vehicle is also provided with a processor module that includes a central processing unit. The processor module is configured to execute program logic for controlling the operation of the unmanned aerial vehicle itself, the image capturing device, the light detection and ranging (LIDAR) sensor, the geolocation devices, the timekeeping device, the memory module, and the communication module of the unmanned aerial vehicle. The program logic is typically a set of computer-readable instructions that are sent to the wireless communication device from the computing device. The computer-readable instruction may include, but is not limited to, an instruction to adjust the flight path of the unmanned aerial vehicle, an instruction to modulate the power of the light detection and ranging (LIDAR) sensor, an instruction to adjust an angle of one of the digital cameras relative to the unmanned aerial vehicle, an instruction to control the rate at which the digital cameras capture images, an instruction on how often data signals must be sent to the database management system and / or the computing device, and the like.

[0061] The database management system typically locates in a server and acts as an interface between a database that forms part of the database management system, the unmanned aerial vehicle, and the computing device. The database management system is configured to receive data signals from the wireless communication device, fuse the data received via the data signals, and manage said data as geospatial metadata. In this manner, the database of the database management system may be updated continuously with real-time or near real-time geospatial metadata. The database management system is also configured to receive data signals from the computing device, fuse the data, and manage said data. Data that is sent from the computing device to the database management system includes, but is not limited to: statistical data on objects, buildings, persons, doctrines, weather patterns, and traffic conditions of the relevant geographical area; attribute data on objects, buildings, persons, doctrines, weather patterns, and traffic conditions of the relevant geographical area; and statistical and / or attribute data on historical threats that have occurred in the relevant geographical area.

[0062] Geospatial metadata and data that is received from the computing device is managed as tables in a relational database.

[0063] The threat assessment system also includes a threat forecasting server that is configured to establish a data connection with the database management system and the computing device. The threat forecasting server is configured to provide identifiers of a possible future threat, the identifiers being graphic representations of attributes of the possible future threat that are calculated and provided by a machine learning model that was obtained by processing type, date, time, and location identifiers of historical threats through a machine learning algorithm, the threat forecasting server comprising: a receiver module that is configured to receive input data from the computing device, the input data taking the form of a request for output data that pertain to a possible future threat that has been specified by a user of the threat assessment system; a memory storage device on which a machine learning model and the input data is stored; a processor module that includes a central processing unit that is configured to retrieve the machine learning model and the input data from the memory storage device, and to process the input data through the machine learning model to provide extrapolated output data, the output data including at least type, time, location, and probability identifiers of the possible future threat; and a communication module that is configured to transmit the output data of the processor module, in a format that is compatible with geographic information system software that has been loaded onto the computing device.

[0064] The receiver module and the communication module may be one and the same. The receiver module and the communication module may be a wireless communication device.

[0065] The machine learning model may be trained by a method including the steps of:

[0066] (i) compiling a database or databases of historical threats, the database or databases comprising at least type, date, time, and location identifiers of the historical threats;

[0067] (ii) retrieving the data (i.e., identifiers) from the database or databases;

[0068] (iii) refining the data to provide refined data;

[0069] (iv) summarizing the data to provide summarized data; and

[0070] (v) processing the summarized data through a machine learning algorithm to provide a machine learning model that is suitable for processing the input data, and extrapolating output data therefrom that correspond to a possible future threat.

[0071] The machine learning algorithm is typically a k-nearest neighbors algorithm, a k- means clustering algorithm, or a linear regression algorithm.

[0072] The computing device is a desktop computer that is operated and maintained by a security agency, such as the national or local police. The desktop computer includes a communication module that is configured to establish a data connection, over the communication network, with the database management system and the wireless communication device of the unmanned aerial vehicle.

[0073] Off the shelf geographic information system software is loaded onto a data storage device of the desktop computer. The geographic information system software is executable by a processor of the desktop computer and configured to retrieve (from the database of the database management system and the database of the threat forecasting server), process, and display (on a monitor of the desktop computer) all or selected geospatial metadata that was obtained by the constituent parts of the unmanned aerial vehicle, and / or statistical data and / or attribute data that were sent from the computing device to the database management system , and / or output data from the threat forecasting server. The user may select the data that must be retrieved by the geographic information system software by using a keyboard and mouse of the desktop computer. The geographic information system software is also configured to provide the user with a list of all data types and sources that are stored on and / or that were retrieved from the database of the database management system and the database of the threat forecasting server.

[0074] Once the data has been retrieved, the geographic information system processes the data and displays each dataset or data class, on the monitor of the desktop computer, as a virtual layer that has been superimposed onto another virtual layer. This greatly facilitates analysis of the data by the user of the threat assessment system.

[0075] For example, a first virtual layer may comprise a graphic representation of the topography of the relevant geographical area. A second virtual layer may be superimposed on the first virtual layer and comprise a graphic representation of street and building names that locate in the relevant geographical area. A third virtual layer may be superimposed onto the first and second virtual layers and comprise a graphic representation of traffic conditions, at a selected time, on the roads that locate in the relevant geographical area. A fourth virtual layer may be superimposed on the first to third virtual layers and comprise a graphic representation of an area within the relevant geographical area that would be affected if a particular threat occurred.

[0076] In use, an authorized user, for example a police officer who is responsible for monitoring and evaluating possible civil aviation threats and threats to an airport, accesses the desktop computer and cause the geographic information software to be executed on the desktop computer. A graphical user interface of the geographic information software is displayed on the monitor of the desktop computer. The police officer can manipulate functionalities of the geographic information software by using the mouse and keyboard of the desktop computer to interact with the graphical user interface of the geographic information system software.

[0077] The geographic information software causes the communication module of the desktop computer to establish a data connection between the desktop computer, the database management system, and the wireless communication device of the unmanned aerial vehicle. Once the data connections have been established, data signals can be exchanged between the desktop computer and the wireless communication device of the unmanned aerial vehicle, and the desktop computer and the database management system. The police officer then sends program logic to the wireless communication device of the unmanned aerial vehicle. The program logic takes the form of a data signal that carries computer-readable instructions that can be executed by the processor module of the unmanned aerial vehicle. The instructions include: a flight path that the unmanned aerial vehicle must take over the airport and regions that surround the airport; the frequency at which the six digital cameras must capture red, green, and blue images and / or videos of the airport, regions that surround the airport, and objects that locate on the premises of the airport or the regions that surround the airport ; the frequency at which the light detection and ranging (LIDAR) sensor must measure distances between the unmanned aerial vehicle and objects and / or surfaces that locate on the premises of the airport and / or regions that surround the airport; the frequency at which the geolocation devices must identify the location, and / or the state, and / or the orientation of the unmanned aerial vehicle; the frequency at which the timekeeping device must identify the date and time; computer-readable instructions that are executable by the processor of the unmanned aerial vehicle for associating each image that has been captured with: o a distance or distances that were measured by the light detecting and ranging (LIDAR) sensor; o a location and state of the unmanned aerial vehicle and its constituent parts; and o a date and time, and storing the associated geospatial metadata in the non-transient computer- readable medium of the unmanned aerial vehicle; the frequency at which the geospatial metadata which were obtained by: o the digital cameras; o the light detection and ranging (LIDAR) sensor; o the geolocation devices; and o the timekeeping device, must be stored on the non-transient computer-readable medium of the platform ; and the frequency at which the geospatial metadata must be sent to the database management system and / or the computing device.

[0078] The geospatial metadata is then sent by the wireless communication device of the unmanned aerial vehicle, over the communication network, to the database management system where it is received, fused, and managed as tables in a relational database.

[0079] The police officer also sends attribute and / or statistical data from the computing device to the database management system where it is received, fused, and managed as tables in a relational database. The attribute and / or statistical data includes, but is not limited to: the airport; the regions that surround the airport; the objects and surfaces that locate on the premises of the airport or on the regions that surround the airport; historical threats that occurred within the airport or the regions that surround it; and historical threats that affected the airport or the regions that surround it.

[0080] The geographic information software also causes the communication module of the desktop computer to establish a data connection between the desktop computer and the threat forecasting server. Once the data connection has been established, the police officer sends a request for identifiers of a possible future threat to the airport and / or civil aviation activities that occur in and / or around the airport. In this example, the police officer sends a request for identifiers of a possible chemical attack on the airport. Using the machine learning model, the threat forecasting server calculates and sends identifiers of the possible chemical attack on the airport to the desktop computer and the geographic information system software. The identifiers include , but is not limited to: global positioning system (GPS) coordinates of an area that would be affected by a blast of the chemical attack; global positioning system (GPS) coordinates of an area over which chemical agents may drift; data on the number of people and / or institutions that would likely encounter the chemical agents; data on the chemical agents used in the chemical attack; and data on the duration of the adverse effects of the chemical agents.

[0081] At this stage, or earlier, the police officer also causes the geographic information system software to display identifiers of data that are stored on the database of the database management system. The identifiers typically comprise a written description of a particular data class or dataset. The user then selects, using the keyboard and mouse of the desktop computer, data on the database that must be retrieved, fused, managed, and processed by the geographic information system software. In this manner, the police officer has full control over which data must be processed by the geographic information system software for analysis.

[0082] The geographic information system software then provides output data that can be analyzed by the police officer. The output data is displayed on the monitor of the desktop computer, and within the graphical user interface of the geographic information system software. The output data is displayed as virtual layers that have been superimposed onto one another. Each virtual layer provides a graphic representation of a dataset or data class that were processed by the geographic information system software. A first virtual layer provides a graphic representation of the topography of the airport and regions that surround it. A second virtual layer provides a graphic representation of street and building names that are located on the premises of the airport and / or regions that surround the airport. A third virtual layer provides a graphic representation of prevailing motor vehicle and commuter traffic conditions. A fourth virtual layer provides a graphic representation of prevailing weather conditions. A fifth virtual layer provides a graphic representation of population density around the airport. It will be appreciated that any number of virtual layers may be superimposed onto one another. The graphic representations take the form of diagrammatic representations, tables, and / or text.

[0083] At this stage, the police officer causes the geographic information system software to superimpose additional virtual layers that provide graphic representations of the attributes of the possible future chemical attack on the airport. In this manner, the threat posed by the possible future chemical attack on the airport, civil aviation activities related to the airport, and regions that surround the airport can be analyzed graphically by the police officer. By retrieving different datasets and / or data classes from the database management system, and processing said data through the geographic information system software, the police officer can analyze a myriad of different scenarios.

[0084] Advantageously, the threat assessment system permits the police officer to collate disjointed data, manage said data, and graphically analyze the data. The system permits the police officer to retrieve, manage, process, and analyze large sets of disjointed data efficiently and in a timely fashion. This is particularly useful during an emergency such a terrorist attack on the airport. The police officer can use the system to obtain geospatial metadata in real time and to quickly analyze a threat posed by the chemical attack and how said threat may evolve over time. This provides the police officer with a so-called bird’s eye view to analyze and recommend appropriate actions that must be taken.

Claims

CLAIMS1. A threat assessment system for retrieving, fusing, managing, processing, and analyzing geospatial metadata, together with attribute and / or statistical data, the threat assessment system comprising: a platform including: o an image capturing device for capturing images of a geographical area that surrounds the image capturing device; o a light detection and ranging (LIDAR) sensor for measuring a distance between the platform and objects and surfaces that surround the platform ; o a geolocation device for identifying a location, and / or a state, and / or an orientation of the platform when an image is captured, and / or a distance is measured; o a timekeeping device for identifying and associating each image that has been captured and / or distance that has been measured with a date and time; o a memory module for storing geospatial metadata of the images that were captured, the distances that were measured, the geographic locations of the platform that were identified, the states of the platform that were identified, the orientations of the platform that were identified, and the dates and times that were identified; o a communication module for receiving data signals from a remote location and / or sending the geospatial metadata to a remote location over a communication network; and io a processor module that is configured to execute program logic for controlling the operation of the image capturing device, the light detection and ranging (LIDAR) sensor, the geolocation device, the timekeeping device, the memory module, and the communication module; a database management system that is configured to: o receive, fuse, and manage the geospatial metadata; o receive, fuse, and manage attribute and / or statistical data of the geographical area, the objects and surfaces that surround the platform, and / or historical data on events that occurred within or had a bearing on the geographical area of interest; and o store the fused datasets in a database; and a computing device including: o a memory module on which an operating system and geographic information system software has been loaded; o a display for displaying a graphical user interface of the geographic information system software; o a computing input device for controlling a functionality of the geographic information system software and selecting geospatial metadata and additional data from the database of the database management system that must be processed by the geographic information system software; o a communication module that is configured to establish a data connection with the database management system and the platform over the communication network;o a processor module that is configured to:■ retrieve the selected geospatial metadata and data from the database of the database management system;■ retrieve the geographic information system software from the memory module; and■ execute the geographic information system software to retrieve, fuse, manage, and process the geospatial metadata together with the selected data from the database of the database management system, and provide output data for analyzing the geospatial metadata together with the data from the database, wherein the output data is displayed on the graphical user interface for analysis by a user of the system as virtual layers that have been superimposed onto one another, and wherein each virtual layer provides a graphic representation of data that have been processed through the geographic information system software. The threat assessment system of claim 1 , wherein the platform is ground vehicle or an unmanned aerial vehicle. The threat assessment system of claim 1 , wherein the image capturing device is a digital camera, an omnidirectional digital camera, or a camera rig that supports six digital cameras. The threat assessment system of claim 1 , wherein the light detection and ranging (LIDAR) sensor uses a phase-shift method to measure distances between the platform and objects and surfaces that surround the platform.The threat assessment system of claim 1 , wherein the platform includes a plurality of geolocation devices, and wherein the geolocation devices are selected from the group comprising a global positioning system (GPS) receiver, an inertial measurement unit (IMU), a gyroscope, a compass, an altimeter, and combinations thereof. The threat assessment system of claim 1 , wherein the communication module of the platform is a wireless communication device that is configured to establish and maintain a data connection, over a communication network, between the wireless communication device and the database management system, and / or the wireless communication device and the computing device. The threat assessment system of claim 6, wherein the processor of the platform is a central processing unit that is configured to instruct the wireless communication device to retrieve data from the memory module of the platform and to transmit said data to the database management system and / or the computing device. The threat assessment system of claim 7, wherein the processor module of the platform is further configured to execute program logic and / or instructions that are sent from the computing device, over the communication network, to the wireless communication device of the platform. The threat assessment system of claim 8, wherein a user of the threat assessment system establishes a data connection between the computingdevice and the platform by sending a data signal from the communication module of the computing device, over the communication network, to the communication module of the platform. The threat assessment system of claim 1 , wherein the database management system acts as an interface between the database of the database management system, the platform, and / or the computing device. The threat assessment system of claim 10, wherein the database of the data management system is updated continuously with data that were obtained in situ by the platform and its constituent parts. The threat assessment system of claim 10, wherein data that are sent to and received by the database management system are stored as tables in a relational database. The threat assessment system of claim 1 , wherein the computing device is selected from the group consisting of a desktop computer, a laptop computer, a tablet, a server, a cloud computing architecture, and combinations thereof. The threat assessments system of claim 1 , wherein the computing input device is selected from the group consisting of a keyboard, a mouse, a touchscreen, a trackball, a microphone, a controller, and combinations thereof.The threat assessment system of claim 1 , wherein the communication module of the computing device is configured to establish data connections with the platform and / or the database management system, over the communication network, via one or more ports using a network protocol. The threat assessment system of claim 1 , wherein the system includes a threat forecasting server that is configured to establish a data connection with the database management system and / or the computing device. The threat assessment system of claim 16, wherein the threat forecasting server is configured to provide identifiers of a possible future threat, the identifiers being graphic representations of attributes of the possible future threat that are calculated and provided by a machine learning model that was obtained by processing type, date, time, and location identifiers of historical threats through a machine learning algorithm, the threat forecasting server comprising: a receiver module that is configured to receive input data from the computing device, the input data taking the form of a request for output data that pertain to a possible future threat that has been specified by a user of the threat assessment system; a memory storage device on which a machine learning model and the input data is stored; a processor module that is configured to retrieve the machine learning model and the input data from the memory storage device, and to process the input data through the machine learning model to provide extrapolatedoutput data, the output data including at least type, time, location, and probability identifiers of the possible future threat; and a communication module that is configured to transmit the output data of the processor module, in a format that is compatible with the geographic information system software, to the computing device. The threat assessment system of claim 17, wherein the machine learning model was trained by a method including the steps of:(i) compiling a database or databases of historical threats, the database or databases comprising at least type, date, time, and location identifiers of the historical threats;(ii) retrieving the data (i.e., identifiers) from the database or databases;(iii) refining the data to provide refined data;(iv) summarizing the data to provide summarized data; and(v) processing the summarized data through a machine learning algorithm to provide a machine learning model that is suitable for processing the input data, and extrapolating output data therefrom that correspond to a possible future threat. The threat assessment system of claim 18, wherein the machine learning algorithm is a k-nearest neighbors algorithm, a k-means clustering algorithm, or a linear regression algorithm.

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