Abstract: A system is configured to receive synthetic aperture radar (SAR) backscatter signatures of a geographical area including the object of interest from a SAR device. The system also extracts feature vectors from the SAR backscatter signature based on the intensity values of the SAR backscatter signature. The system inputs the one or more feature vectors into a neural network model. The system receives, as output from the neural network model, coordinate values indicating one or more visual features of the object of interest. Using these coordinate values, the system determines one or more measurements of the object of interest.

Abstract: A system accesses a plurality of ratio values for similar AOIs, each ratio value indicating a ratio between a count of device users at a similar AOI and a count of vehicles of the similar AOI. The similar AOIs are AOIs that have measurable characteristics within a range of similarity with those of the target AOI. The count of vehicles of the similar AOI is generated using aerial imagery received for the similar AOI. The count of device users at the similar AOI is extracted from third party data for the similar AOI. The system accesses a count of a number of reported device users at the target AOI, and generates an estimate of the vehicle count for the target AOI using the count of the number of reported device users at the target AOI and a combination of the plurality of ratio values for similar AOIs.

Abstract: A system and method are disclosed for receiving a request to track data of an area of interest (AOI). The system receives a plurality of geolocation data records corresponding to a plurality of mobile devices. The system determines which mobile devices of the plurality of mobile devices have visited the AOI. For each mobile device determined to have visited the AOI, the system determines a path of the mobile device. The path including a prior location of the mobile device and a next location of the mobile device. The system transmits for display by a client device, a report including the path for each mobile device determined to have visited the AOI. The path indicating an identification of additional AOIs based on the prior location and the next location.

Abstract: A method comprises receiving an area of interest (AOI) selection. The method further comprises accessing an AOI device location data for the AOI, the AOI device location data indicating locations of devices over time received within the AOI. The AOI device location data is filtered to only include the device location data that match one or more characteristics. A proximity zone is determined for the for the AOI that includes the area of the AOI. A zone device location data for the proximity zone is determined, which indicates locations of devices over time reported within the proximity zone. The method further comprises normalizing the filtered AOI device location data by computing a ratio of the filtered AOI device location data and the zone device location data to generate an AOI user estimate, and transmitting the AOI user estimate to a client device of a requestor.

Abstract: A system and method are disclosed for receiving a request to track data of an area of interest (AOI). The system receives a plurality of geolocation data records corresponding to a plurality of mobile devices. The system determines which mobile devices of the plurality of mobile devices have visited the AOI. For each mobile device determined to have visited the AOI, the system determines a path of the mobile device. The path including a prior location of the mobile device and a next location of the mobile device. The system transmits for display by a client device, a report including the path for each mobile device determined to have visited the AOI. The path indicating an identification of additional AOIs based on the prior location and the next location.

Abstract: A system accesses a plurality of ratio values for similar AOIs, each ratio value indicating a ratio between a count of device users at a similar AOI and a count of vehicles of the similar AOI. The similar AOIs are AOIs that have measurable characteristics within a range of similarity with those of the target AOI. The count of vehicles of the similar AOI is generated using aerial imagery received for the similar AOI. The count of device users at the similar AOI is extracted from third party data for the similar AOI. The system accesses a count of a number of reported device users at the target AOI, and generates an estimate of the vehicle count for the target AOI using the count of the number of reported device users at the target AOI and a combination of the plurality of ratio values for similar AOIs.

Abstract: A system and a method are disclosed for identifying features in a geographic area captured by a sensor. A server transmits computer readable instructions to a client device to cause the client device to display a first graphical element including data entry user interface elements for inputting one or more parameters. The server receives one or more data inputs corresponding to the one or more parameters. In response to receipt of the data inputs, the server transmits computer readable instructions to cause the client device to display a timeline graphical user interface element identifying detections of a feature in data captured for the geographic area. The data captured for the geographic area is selected based on the identified parameters and the timeline graphical user interface is segment into multiple display elements based on the identified parameters.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. The method includes receiving a first image of a geographical area having a first resolution. The method transmits the first image to a machine learning model to identify an area of interest containing an object of interest. The method receives a second image of the geographical area having a second resolution higher than the first resolution. The method transmits the second image to the machine learning model to determine a likelihood that the area of interest contains the object of interest. The method trains the machine learning model to filter out features corresponding to the area of interest in images having the first resolution if the likelihood is below a threshold. The method transmits a visual representation of the object of interest to a user device if the likelihood exceeds the threshold.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. An image of an object of interest is received from the aerial imaging device. A parameter vector is extracted from the image. Image analysis is performed on the image to determine a height and a width of the object of interest. Idealized images of the object of interest are generated using the extracted parameter vector, the determined height, and the determined width of the object of interest. Each idealized image corresponds to a distinct filled volume of the object of interest. The received image of the object of interest is matched to each idealized image to determine a filled volume of the object of interest. Information corresponding to the determined filled volume of the object of interest is transmitted to a user device.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. A moving vehicle analysis system receives images from an aerial imaging device. The system may perform edge analysis in the images to identify a pairs of edges corresponding to a road. The system may identify pixel blobs in the images including adjacent pixels matching each other based on a pixel attribute. The system uses a machine learning model for generating an output identifying moving vehicles in the images. The system determines a count of the moving vehicles captured by the images, where each moving vehicle is associated with corresponding pixel blobs.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. The method includes receiving a first image of a geographical area having a first resolution. The method transmits the first image to a machine learning model to identify an area of interest containing an object of interest. The method receives a second image of the geographical area having a second resolution higher than the first resolution. The method transmits the second image to the machine learning model to determine a likelihood that the area of interest contains the object of interest. The method trains the machine learning model to filter out features corresponding to the area of interest in images having the first resolution if the likelihood is below a threshold. The method transmits a visual representation of the object of interest to a user device if the likelihood exceeds the threshold.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. The method includes receiving a first image of a geographical area having a first resolution. The method transmits the first image to a machine learning model to identify an area of interest containing an object of interest. The method receives a second image of the geographical area having a second resolution higher than the first resolution. The method transmits the second image to the machine learning model to determine a likelihood that the area of interest contains the object of interest. The method trains the machine learning model to filter out features corresponding to the area of interest in images having the first resolution if the likelihood is below a threshold. The method transmits a visual representation of the object of interest to a user device if the likelihood exceeds the threshold.

Abstract: Disclosed is a method and system for processing images from an aerial imaging device. An image of an object of interest is received from the aerial imaging device. A parameter vector is extracted from the image. Image analysis is performed on the image to determine a height and a width of the object of interest. Idealized images of the object of interest are generated using the extracted parameter vector, the determined height, and the determined width of the object of interest. Each idealized image corresponds to a distinct filled volume of the object of interest. The received image of the object of interest is matched to each idealized image to determine a filled volume of the object of interest. Information corresponding to the determined filled volume of the object of interest is transmitted to a user device.