A system may be configured to collect geospatial features (in vector form) such that a software application is operable to edit an object represented by at least one vector. Some embodiments may: generate, via a trained machine learning model, a pixel map based on an aerial or satellite image; convert the pixel map into vector form; and store the vectors. This conversion may include a raster phase and a vector phase. A system may be configured to obtain another image, generate another pixel map based on the other image, convert the other pixel map into vector form, and compare the vectors to identify changes between the images. Some implementations may cause identification, based on a similarity with converted vectors, of a more trustworthy set of vectors for subsequent data source conflation.

Techniques are disclosed for sharing sensor information between multiple vehicles. A system for sharing sensor information between multiple vehicles, can include an aerial vehicle including a first computing device and first scanning sensor, and a ground vehicle including a second computing device and second scanning. The aerial vehicle can use the first scanning sensor to obtain first scanning data and transmit the first scanning data to the second computing device. The ground vehicle can receive the first scanning data from the first computing device, obtain second scanning data from the second scanning sensor, identify an overlapping portion of the first scanning data and the second scanning data based on at least one reference object in the scanning data, and execute a navigation control command based on one or more roadway objects identified in the overlapping portion of the first scanning data and the second scanning data.

An apparatus for providing drone data provides a method of matching a user who needs drone data of a certain area with at least one provider capable of providing drone data of a part or the entirety of the certain area.

An imaging system can include a first and second camera configured to capture first and second sets of oblique images along first and second scan paths, respectively, on an object area. A drive is coupled to a scanning mirror structure, having at least one mirror surface, and configured to rotate the structure about a scan axis based on a scan angle. The first and second cameras each have an optical axis set at an oblique angle to the scan axis and include a respective lens to focus first and second imaging beams reflected from the mirror surface to an image sensor located in each of the cameras. The first and second imaging beams captured by their respective cameras can vary according to the scan angle. Each of the image sensors captures respective sets of oblique images by sampling the imaging beams at first and second values of the scan angle.

Systems and methods for aggregating and integrating distributed grid element inputs are disclosed. A data platform is provided for a distribution power grid. The data platform provides a crowd-sourced gaming system for identifying grid elements and determining dynamic electric power topology. The data platform also provides an interactive interface for displaying a view of a certain area with identified grid elements. The data platform communicatively connects to the identified grid elements, collects data from the identified grid elements, and manages the distribution power grid.

Provided are a road network extraction method, a device, and a storage medium, which relate to the technical field of artificial intelligence and, in particular, to the fields of image processing, computer vision, and the like and are specifically applicable to scenarios such as intelligent transportation and a smart city. A specific implementation scheme includes: extracting a first road network of a target region according to user trajectories of the target region; extracting a second road network of the target region according to a satellite aerial image of the target region; and extract a target road network of the target region according to the first road network, the second road network, and the user trajectories. Efficient and accurate road network extraction can be achieved through techniques in embodiments of the present disclosure.

According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising an imagery data source storing image data from a plurality of images; a ground point module; a memory storing program instructions; and a ground point processor, coupled to the memory, and in communication with the ground point module and operative to execute the program instructions to: receive image data for an area of interest (AOI); generate a digital surface map from the received image data, wherein the digital surface map includes an elevation value for each of a plurality of points on the digital surface map; generate a ground point sampling based on the elevation values for the plurality of points on the digital surface map; generate an image boundary sampling based on elevation values for the plurality of points along a plurality of edges of the area of interest; and interpolate the generated ground point sampling and the image boundary sampling to generate a digital terrain map. Numerous other aspects are provided.

A method includes determining an altitude of a camera of an aerial vehicle, determining a field of view (FOV) of a camera, generating a localized map, determining a relative position of the aerial vehicle on the localized map, and determining a relative heading of the aerial vehicle.

The invention relates to a method of encoding image data representing a geographical line, GL, or its surroundings, and a data processing system being configured to perform the encoding. The method comprises applying a data compression process to reduce the spatial resolution of a given image represented by the image data. Specifically, the image may form an imagery layer of an imagery stack comprising various imagery layers, each imagery layer representing an individual image, e.g. of a same geographical region. The data compression process comprises: generating two or more sublayers of the image by resampling at least one region of the image at a respective lower spatial resolution than its original spatial resolution, while maintaining or resampling the spatial resolution of at least one other region of the image such that, when the sublayers are overlapped to jointly define a combined multi-resolution representation of the image, the average resolution of the combined multi-resolution representation is lower than the original resolution of the image.

A method and system of constructing a network map from imagery comprising using an iterative search process guided by a CNN-based decision function to derive a network graph directly from the output of the CNN.