A method including receiving a spatial query on spatial data. The spatial query has a spatial query extent including a sub-portion of the spatial data. A projection type is selected for the spatial query. A framebuffer is created for the selected projection type. Vertex buffers are established to hold a geometry of the selected projection type. The vertex buffers are passed from a CPU to a GPU. A spatial geometry of the spatial query extent is rendered into the framebuffer by projecting feature vertex data for features that fall at least partly within the spatial query extent into the vertex buffers. Rendering generates rendered framebuffer pixel values. Pixel values of the rendered framebuffer are retrieved as bytes on the CPU. A spatial query result is processed that includes or uses the pixel values.

Systems and methods are disclosed for transporting people using air vehicles.

The possibility of a work machine 40 being affected by a disaster in a second designated area including an existence position of the work machine 40 is predicted based on an amount of rainfall in a first designated area. A hazard map representing a result of the prediction of the possibility of the work machine 40 being affected by a disaster in the second designated area is outputted to a remote output interface 220 in a remote operation apparatus 20 (a client) (or a management output interface 620 in a management client 60). Accordingly, a user can take measures to reduce the possibility of the work machine being affected by a disaster, for example, to communicate with the persons involved in order to move the work machine 40 from a current position.

A high-precision map construction method, apparatus, and electronic device are provided. The method can include: displaying a first color image corresponding to a first track point; according to a first color sub-image and a depth image corresponding to the first track point, obtaining point cloud data corresponding to the first sub-color image, wherein the first sub-color image is a sub-image corresponding to an element to be added in the first color image, and the element to be added is an element to be added in a high-precision map for display; extracting a bounding box corresponding to the point cloud data; and generating a newly-added three-dimensional element according to the bounding box in the high-precision map.

A high-precision map construction method, apparatus, and electronic device are provided. The method can include: displaying a first color image corresponding to a first track point; according to a first color sub-image and a depth image corresponding to the first track point, obtaining point cloud data corresponding to the first sub-color image, wherein the first sub-color image is a sub-image corresponding to an element to be added in the first color image, and the element to be added is an element to be added in a high-precision map for display; extracting a bounding box corresponding to the point cloud data; and generating a newly-added three-dimensional element according to the bounding box in the high-precision map.

An improved UAV system and methods for operation in an inventory management system. The methods include generating a three dimensional (3D) map and estimating a position and orientation of the UAV based upon this map; autonomously navigating the UAV in the environment by using the generated 3d map in conjunction with the position and the orientation of the UAV; performing static and dynamic obstacle avoidance in the environment using collision avoidance; and finding the optimal path from a source node to a destination node within the environment.

An improved UAV system and methods for operation in an inventory management system. The methods include generating a three dimensional (3D) map and estimating a position and orientation of the UAV based upon this map; autonomously navigating the UAV in the environment by using the generated 3d map in conjunction with the position and the orientation of the UAV; performing static and dynamic obstacle avoidance in the environment using collision avoidance; and finding the optimal path from a source node to a destination node within the environment.

Described herein are methods and systems for real-time swiftwater risk category distributed mapping. A mobile computing device generates a request for swiftwater risk information, the request including a location. A server computing device receives the request for swiftwater risk information from the mobile computing device. The server computing device models hydrologic conditions for a plurality of segments of one or more bodies of water at or near the location. The server computing device classifies each segment of the bodies of water according to a level of potential risk of hazards associated with the hydrologic conditions. The server computing device generates a visual representation of the bodies of water that includes a classification indicator for one or more of the plurality of segments for display on the mobile computing device, and transmits the visual representation to the mobile computing device.

Described herein are methods and systems for real-time swiftwater risk category distributed mapping. A mobile computing device generates a request for swiftwater risk information, the request including a location. A server computing device receives the request for swiftwater risk information from the mobile computing device. The server computing device models hydrologic conditions for a plurality of segments of one or more bodies of water at or near the location. The server computing device classifies each segment of the bodies of water according to a level of potential risk of hazards associated with the hydrologic conditions. The server computing device generates a visual representation of the bodies of water that includes a classification indicator for one or more of the plurality of segments for display on the mobile computing device, and transmits the visual representation to the mobile computing device.

Embodiments provide systems and methods for three-dimensional building generation from machine learning and topological models. The method uses topology models that are converted into vertices and edges. A BGAN (Building generative adversarial network) is used to create fake vertices/edges. The BGAN is then used to generate random samples from seen sample of different structures of building based on relationship of vertices and edges. The embeddings are then fed into a machine trained network to create a digital structure from the image.