Road Network Extraction from Satellite Images Using CNN Based Segmentation and Tracing

Drawing and updating road networks are both time-consuming and labor-intensive. Deep learning technology and high-resolution remote sensing images have provided opportunities for automatic road extraction. However, recent convolutional neural network (CNN) based segmentation methods have shown serious problems on connectivity; road tracing methods with single starting point perform well in connectivity but often result in part areas unreached. We propose a multiple starting points tracer which benefits from both segmentation and tracing methods. We compare our approach with most recent tracing methods on satellite images of global cities and find that our method achieves 8% improvement on IoU.