Road Topology Extraction From Satellite Imagery by Joint Learning of Nodes and Their Connectivity

Road topology extraction from satellite images, which has long been of interest, is an essential task in remote sensing. The graph representation of road networks is one of the most challenging aspects of road topology extraction. Most existing approaches cast road extraction as binary segmentation and then use postprocessing, such as skeletonization, to infer networks from pixelwise prediction. In our work, we believe that a road network can be represented by an undirected graph denoted as
$G =$
(
$V$
,
$E$
), where
$V$
and
$E$
represent the set of road nodes and the set of edges between nodes, respectively. Thus, to construct the road topology, we propose NodeConnect, a new method of extracting nodes for a road network and inferring the connectivity between nodes. A convolutional neural network is jointly trained to predict the nodes and connectivity map for nodes, and the edges between nodes are inferred from the connectivity map. We compare our approach with several segmentation methods on the DeepGlobe and RoadTracer datasets. The experiments show that our approach achieves state-of-the-art performance in terms of pixel-based metrics and topological precision and recall.