Spatial information inference net: Road extraction using road-specific contextual information

Deep neural networks perform well in road extraction from very high-resolution satellite imagery. A network with certain reasoning ability will give more satisfactory road network extraction results. In this study, we designed a spatial information inference structure, which enables multidirectional message passing between pixels when it is integrated to a typical semantic segmentation framework. Since the spatial information could be propagated and reinforced via inter layer propagation, the proposed road extraction network can learn both the local visual characteristics of the road and the global spatial structure information (such as the continuity and trend of the road). As a result, this method can effectively solve occlusions and preserve the continuity of the extracted road. The validation experiments using three large datasets of very high-resolution (VHR) satellite imagery show that the proposed method can improve road extraction accuracy and provide an output that is more in line with human expectations.