ROAD EXTRACTION FROM SATELLITE IMAGE VIA AUXILIARY ROAD LOCATION PREDICTION

Road extraction from satellite images is usually corrupted with several disconnected segments so that it does not satisfy the real application. The segmentation-based methods fail to correct separated roads due to the incompleteness information. Therefore, this paper introduces auxiliary Road Location Prediction(RLP), a task leveraging global context information to help road segmentation infer each road segment. The auxiliary task has two branches: horizontal location prediction and vertical location prediction which can predict locations of all the roads. By combining road segmentation and RLP, road extraction performance is effectively improved. As a result, the additional training signals help the primary road segmentation task to aggregate surrounding scene information to reason about its connectivity. The experiments on two public datasets have demonstrated the effectiveness of the proposed method.