Abstract: According to various embodiments, a system for detecting information about a road relating to digital geographical map data is provided. The system comprises an input device configured to obtain remotely captured geographical image data; and a processor configured to generate ground truth image data from the digital geographical map data, and generate binary image data of the road segments from the remotely captured geographical image data using a semantic segmentation task. The processor is further configured to: skeletonize the binary image data to generate skeletonized binary image data including a center line of each road segment of the road segments, detect a road segment missing from the digital geographical map data using the skeletonized binary image data, detect a road width from the binary image data and the center line of each road segment of the road segments; and detect number of lanes from the detected road width.

Abstract: Computer-implemented training method of training a map segmenter including a deep neural network, including: providing a training dataset including training image data including training pairs of map images of a geographical area acquired by one or more image acquisition apparatuses and corresponding segmentation masks, wherein the training image data may be stored a computer memory; generating synthetic map images by a computer-implemented generation method including creating synthetic map images by applying a generative adversarial network onto segmentation masks, wherein the segmentation masks may include the corresponding segmentation masks and additional segmentation masks; storing the synthetic map images and the corresponding additional segmentation masks as additional training data pairs in the training dataset in the computer memory; and training the map segmenter with the training dataset.

Abstract: A method of predicting one or more road segment attributes corresponding to a road segment in a geographical area, the method including: providing trajectory data and satellite image of the geographical area; calculating one or more image channels based on the trajectory data; and using at least one processor, classi-fying the road segment based on the one or more image channels and the satellite image using a trained classifier into prediction probabil-ities of the road attributes A data processing system including one or more processors configured to carry out a the method of predicting road attributes. A computer executable code including instructions for predicting one or more road segment attributes according to the method.

Abstract: A method of predicting one or more road segment attributes corresponding to a road segment in a geographical area, the method including: providing trajectory data and satellite image of the geographical area; calculating one or more image channels based on the trajectory data; and using at least one processor, classifying the road segment based on the one or more image channels and the satellite image using a trained classifier into prediction probabilities of the road attributes A data processing system including one or more processors configured to carry out a the method of predicting road attributes. A computer executable code including instructions for predicting one or more road segment attributes according to the method.

Abstract: A method of predicting one or more road segment attributes corresponding to a road segment in a geographical area, the method including: providing trajectory data and satellite image of the geographical area; calculating one or more image channels based on the trajectory data; and using at least one processor, classifying the road segment based on the one or more image channels and the satellite image using a trained classifier into prediction probabilities of the road attributes A data processing system including one or more processors configured to carry out a the method of predicting road attributes. A computer executable code including instructions for predicting one or more road segment attributes according to the method.