An acquisition circuit acquires first position information indicating a position and a first image captured by a camera at the position indicated by the first position information. A memory stores second position information indicating a prescribed position on a map and feature information extracted from a second image corresponding to the prescribed position. The second position information is associated with the feature information. A processor estimates the position indicated by the first position information on the basis of the second position information in the case that the position indicated by the first position information falls within a prescribed range from the prescribed position indicated by the second position information and the first image corresponds to the feature information.

To provide navigation directions according to road surface types of road segments, a request for navigation directions from a starting location to a destination location is received. A set of candidate routes for navigating from the starting location to the destination location is identified. Then for each road segment within each candidate route, a road surface type for the road segment is determined. A route is selected from the set of candidate routes based at least in part on the road surface types of the road segments within the route. A set of navigation directions is provided for presentation on a client device for navigating from the starting location to the destination location via the selected route.

A method, an apparatus, and a device for classifying LiDAR point cloud data, and a storage medium. The method includes: acquiring sample point cloud data and LiDAR point cloud data to be classified; building a point cloud classifier according to the sample point cloud data; classifying the LiDAR point cloud data to be classified by the point cloud classifier. In the present disclosure, the point cloud classifier is trained from the sample point cloud data, and the automatic classification is performed by the point cloud classifier for the LiDAR point cloud data to be classified. The sample point cloud data used for training the point cloud classifier includes various tower data and electric power line data. Moreover, after the classification result is obtained from the automatic classification, the speckling merging optimization is further performed, and the optimization is performed according to the tower position file and the preset optimization rule.

A system for vector extraction comprising a vector extraction engine stored and operating on a network-connected computing device that loads raster images from a database stored and operating on a network-connected computing device, identifies features in the raster images, and computes a vector based on the features, and methods for feature and vector extraction.

A system for instructing a user on repairing a vehicle configured to (i) receive, from the user, a request to repair a vehicle, including information about the vehicle; (ii) present, to the user via a user computer device, a user interface to allow the user to search for a repair facility to repair the vehicle; (iii) receive, from the user via the user interface, a selection of a repair facility; (iv) determine whether the selected repair facility is a select service location, where a select service location is a pre-authorized repair facility; and (v) if the selected repair facility is a select service location, transfer the information about the vehicle to a computer device associated with the selected repair facility.

A method for generating a road annotation, a device, and a storage medium are provided. The method may include: generating a road quantity and a road width in a tag picture; generating, for each road in the tag picture, a start point and an end point of the road; generating at least one point between the start point and the end point; drawing, for two adjacent points, a line segment from a previous point to a next point, where a width of the line segment is equal to the road width; and generating slanted box annotation information based on a coordinate of the previous point and a coordinate of the next point, where the slanted box annotation information includes an intersection point of diagonal lines, a width, a height and a slant angle of a slanted box.

A system includes an aerially mounted video capture device and a processor. The processor is operable to direct the video capture device to obtain an image of a monitored area and process the image to identify objects of interest represented in the image. The processor is also operable to generate bounding perimeter virtual objects for the identified objects of interest, which substantially surround their respective objects of interest. The processor is further operable to determine danger zones for the identified objects of interest based on the bounding perimeter virtual objects. Each danger zone represents a distance threshold about a respective object of interest. The processor is further operable to determine at least one near-miss condition based at least in part on an actual or predicted overlap of danger zones for two or more objects of interest, and to generate an alert at least partially in response to the near-miss condition.

Disclosed is a technique for a railway disaster monitoring system for monitoring a foreign matter on a railway, which includes a camera and an image processing unit that receives a railway image captured by the camera. The image processing unit includes a segmented image acquisition unit that obtains a plurality of segmented images including a rail from the railway image received from the camera and scales the segmented images to obtain segmented image blocks of a predetermined size, and a segmented image determination unit which includes deep neural network (DNN) discriminators trained with deep learning neural networks and inputs the segmented image blocks to the DNN discriminators to determine whether the railway is in a normal state in which there is no foreign matter on the railway except a train passing by. Using the segmented images, a foreign matter on a railway can be rapidly and easily determined using a deep learning neural network even with a small number of resources.

A method includes receiving a satellite image of an area and classifying each pixel in the satellite image as representing water, land or unknown using a model. For each of a plurality of possible water levels, a cost associated with the water level is determined, wherein determining the cost associated with a water level includes determining a number of pixels for which the model classification must change to be consistent with the water level and determining a difference between the water level and a water level determined for the area at a previous time point. The lowest cost water level is selected and used to reclassify at least one pixel.

A road network data processing method, an electronic device, and a storage medium are provided, and relates to the field of the intelligent transportation. The method includes: determining a target road surface in a road image, wherein the target road surface is a road surface matching a road line in first road network data, the road image is an image, for a road, obtained according to a satellite image, and the first road network data is original road network data of a basic base map of an electronic map; and adjusting the road line to a road surface corresponding to a road width of the target road surface, to obtain second road network data of the basic base map.