Automated Openstreetmap Data Alignment for Road Network Mapping

OpenStreetMap(OSM) provides extensive coverage of road network that can be a source to prepare training samples for automated road mapping using very high resolution(VHR) satellite images and machine learning. However, several studies have shown that the pervasive spatial misalignment between OSM vector data and VHR images yields poor quality training samples and thereby compromises the performance of models. In this study, we undertake to address this shortcoming and develop an automated line segment shifting workflow to yield OSM vector data that aligns with VHR road features to generate high quality training samples. The approach leverages the standard deviation differences of road pixels and background information to guide the transformations. By taking into account trees, shadows, cars and water body on or beside the road when STD was calculated, our method is robust to various road obstacles. Experimental validations are conducted to confirm the correctness of aligned OSM data showing up to 338% improvement compared with original OSM. Finally, based on visual inspection, the road map generated by aligned OSM also presents obvious quality improvement in comparison with map created by original OSM.