BT-RoadNet: A boundary and topologically-aware neural network for road extraction from high-resolution remote sensing imagery

Abstract Automatic road extraction from high-resolution remote sensing imagery has various applications like urban planning and automatic navigation. Existing methods for automatic road extraction however, focus on regional accuracy but not on the boundary quality; and most of these road extraction methods yield discontinuous results due to noise and occlusions. To address these two problems, a Boundary and Topological-aware Road extraction Network (BT-RoadNet) is proposed. BT-RoadNet is a coarse-to-fine architecture composed of two encoder-to-decoder networks, a Coarse Map Predicting Module (CMPM) and Fine Map Predicting Module (FMPM). The CMPM learns to predict coarse road segmentation maps, in which a Spatial Context Module (SCM) is employed as a bridge to solve discontinuous problems. The FMPM is used to refine the coarse road maps by learning the difference between the coarse road extraction result and the ground truth. Experiments were conducted on the open Massachusetts Road Dataset, a newly annotated Wuhan University (WHU) Road Dataset, and three large satellite images. Quantitative and qualitative analysis demonstrate that the proposed BT-RoadNet can enhance road network extraction to deal with interruptions caused by shadows and occlusions, extract roads with different scales and materials, and handle roads under construction that have incomplete spectral and geometric properties.