An object identification system accesses a first ground truth image comprising an object of interest. The object identification system determines a relative size of the object of interest based on a comparison of a size of the object of interest to an overall size of the first ground truth image. In response to determining that the relative size of the object of interest does not exceed the threshold size, the object identification system generates a synthetic image by copying the OI into a second ground truth image, the second ground truth image different from the first ground truth image. The object identification system updates a training dataset of images with the synthetic image, wherein the training dataset is applied to train a machine-learned model to identify the OI in an image of a test dataset.
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 10/422 - Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation for representing the structure of the pattern or shape of an object therefor
An object identification system accesses a first ground truth image comprising an object of interest. The object identification system determines a relative size of the object of interest based on a comparison of a size of the object of interest to an overall size of the first ground truth image. In response to determining that the relative size of the object of interest does not exceed the threshold size, the object identification system generates a synthetic image by copying the OI into a second ground truth image, the second ground truth image different from the first ground truth image. The object identification system updates a training dataset of images with the synthetic image, wherein the training dataset is applied to train a machine-learned model to identify the OI in an image of a test dataset.
G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 20/17 - Terrestrial scenes taken from planes or by drones
G06T 7/62 - Analysis of geometric attributes of area, perimeter, diameter or volume
A device receives an AOI selection that indicates a geographic area. The device accesses AOI device location data for the AOI that indicates locations of plural devices detected within the AOI. The device sets a device penetration rate configuration based on at least one characteristic of the AOI. The device determines respective weighting factors for each of the plural devices detected within the AOI based on one or more device penetration rates corresponding to the set device penetration rate configuration. Each of the one or more device penetration rates indicating, for a corresponding geographic region, a number of devices with home locations within the geographic region divided by a population of the geographic region. The device generates an estimate of a number of users in the AOI based on the plural devices detected within the AOI, and the respective weighting factors. The device transmits the estimate to a client device.
A device receives an AOI selection that indicates a geographic area. The device accesses AOI device location data for the AOI that indicates locations of plural devices detected within the AOI. The device sets a device penetration rate configuration based on at least one characteristic of the AOI. The device determines respective weighting factors for each of the plural devices detected within the AOI based on one or more device penetration rates corresponding to the set device penetration rate configuration. Each of the one or more device penetration rates indicating, for a corresponding geographic region, a number of devices with home locations within the geographic region divided by a population of the geographic region. The device generates an estimate of a number of users in the AOI based on the plural devices detected within the AOI, and the respective weighting factors. The device transmits the estimate to a client device.
A system and method are disclosed for determining a classification and sub-classification of an aircraft. The system receives an aerial image of a geographic area that includes one or more aircrafts. The system inputs the aerial image into a machine learning model. The system receives an output from the machine learning model for each aircraft of the one or more aircrafts. Based on the output for each aircraft, the system determines a set of geometric measurements. The system compares the set of geometric measurements to a plurality of known sets of geometric measurements. Based on the comparison, the system identifies a known set of geometric measurements from the plurality of known sets of geometric measurements. The known set is mapped by a database to a sub-classification. The system outputs the sub-classification.
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.
G06F 16/909 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
H04W 4/02 - Services making use of location information
H04W 4/021 - Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
H04W 36/24 - Reselection being triggered by specific parameters
H04W 36/32 - Reselection being triggered by specific parameters by location or mobility data, e.g. speed data
H04W 60/04 - Affiliation to network, e.g. registrationTerminating affiliation with the network, e.g. de-registration using triggered events
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.
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.
G01S 13/90 - Radar or analogous systems, specially adapted for specific applications for mapping or imaging using synthetic aperture techniques
G01S 7/41 - Details of systems according to groups , , of systems according to group using analysis of echo signal for target characterisationTarget signatureTarget cross-section
9.
Dynamic graphical user interface for analyzing sensor captured data
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.
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G06K 9/46 - Extraction of features or characteristics of the image
G06F 16/587 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
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.
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
11.
JOINT MODELING OF OBJECT POPULATION ESTIMATION USING SENSOR DATA AND DISTRIBUTED DEVICE DATA
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.
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 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.
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.
G06F 16/909 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
H04W 4/02 - Services making use of location information
H04W 4/021 - Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
H04W 36/24 - Reselection being triggered by specific parameters
H04W 36/32 - Reselection being triggered by specific parameters by location or mobility data, e.g. speed data
H04W 60/04 - Affiliation to network, e.g. registrationTerminating affiliation with the network, e.g. de-registration using triggered events
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.
G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
G06F 16/909 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
G06K 9/32 - Aligning or centering of the image pick-up or image-field
H04W 4/02 - Services making use of location information
H04W 4/021 - Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
H04W 36/24 - Reselection being triggered by specific parameters
H04W 36/32 - Reselection being triggered by specific parameters by location or mobility data, e.g. speed data
H04W 60/04 - Affiliation to network, e.g. registrationTerminating affiliation with the network, e.g. de-registration using triggered events
15.
Remote determination of containers in geographical region
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.
Large scale analysis of data in the fields of business,
economic information regarding natural resources,
transportation logistics, and government affairs, for
purposes of understanding socio-economic trends;
subscriptions to computer services relating to data and data
analysis; business consulting and information services.
Large scale analysis of data in the fields of business, economic information regarding natural resources, transportation logistics, and government affairs, for purposes of understanding socio-economic trends; Subscriptions to computer services relating to data and data analysis; Business consulting and information services
45 - Legal and security services; personal services for individuals.
Goods & Services
Large scale analysis of data concerning physical security and security of property in the fields of geospatial data analytics, movement of goods, consumer behavior, and energy and natural resource supply and demand, in order to understand socio-economic trends and for government national security and intelligence purposes; Consulting regarding regulatory compliance and obtaining permitting in the fields of geospatial data analytics, movement of goods, consumer behavior, and energy and natural resource supply and demand
19.
Remote determination of containers in geographical region
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.
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.
G06K 9/46 - Extraction of features or characteristics of the image
G06K 9/62 - Methods or arrangements for recognition using electronic means
G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
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.
G06K 9/46 - Extraction of features or characteristics of the image
G06K 9/62 - Methods or arrangements for recognition using electronic means
G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
G06T 7/55 - Depth or shape recovery from multiple images
G06T 7/62 - Analysis of geometric attributes of area, perimeter, diameter or volume
G06N 99/00 - Subject matter not provided for in other groups of this subclass
G06T 7/73 - Determining position or orientation of objects or cameras using feature-based methods
G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
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.
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.
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.
H04N 5/341 - Extracting pixel data from an image sensor by controlling scanning circuits, e.g. by modifying the number of pixels having been sampled or to be sampled
H01L 51/42 - Solid state devices using organic materials as the active part, or using a combination of organic materials with other materials as the active part; Processes or apparatus specially adapted for the manufacture or treatment of such devices, or of parts thereof specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation
H04N 5/347 - Extracting pixel data from an image sensor by controlling scanning circuits, e.g. by modifying the number of pixels having been sampled or to be sampled by combining or binning pixels in SSIS
H04H 20/53 - Arrangements specially adapted for specific applications, e.g. for traffic information or for mobile receivers
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.
Large scale analysis of consumer, business and environmental
data for purposes of understanding socio-economic trends for
business purposes and government affairs; subscriptions to
computer services relating to consumer, business and
environmental data and data analysis; business consulting
and information services.
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Large scale analysis of data for purposes of understanding socio-economic trends for business purposes and government affairs in the fields of retail, road and traffic information, crude oil inventories, water reserves, economic forecasting and analysis, poverty mapping and agriculture and other similar areas where large scale data analysis can provide insights into social and economic changes and trends; software-as-a-service, namely subscriptions to computer services in the field of data and data analysis for purposes of understanding socio-economic trends for business purposes and government affairs in the fields of retail, road and traffic information, crude oil inventories, water reserves, economic forecasting and analysis, poverty mapping and agriculture and other similar areas where large scale data analysis can provide insights into social and economic changes and trends; business consulting and information services namely providing large scale data analysis for providing insight into socio-economic trends for business purposes and government affairs in the fields of retail, road and traffic information, crude oil inventories, water reserves, economic forecasting and analysis, poverty mapping and agriculture and other similar areas where large scale data analysis can provide insights into social and economic changes and trends
45 - Legal and security services; personal services for individuals.
Goods & Services
Large scale analysis of data for purposes of understanding socio-economic trends for business purposes and government affairs; Subscriptions to computer services relating to data and data analysis; Business consulting and information services Large scale analysis of data for purposes of understanding socio-economic trends for government national security and intelligence purposes, and regulatory compliance and permitting therefor
