Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images

Abstract

Road network extraction plays a critical role in data updating, urban development, and decision support. To improve the efficiency of labeling road datasets and addressing the problems of traditional methods of manually extracting road networks from high-resolution images, such as their slow speed and heavy workload, this paper proposes a semi-automatic method of road network extraction from high-resolution remote-sensing images. The proposed method needs only a few points to extract a single road in the image. After the roads are extracted one by one, the road network is generated according to the width of each road and the spatial relationships among the roads. For this purpose, we use regional growth, morphology, vector tracking, vector simplification, endpoint modification, road connections, and intersection connections to generate road networks. Experiments on four images with different terrains and different resolutions show that this method has high extraction accuracy under different image conditions. The comparisons with the semi-automatic GVF-snake method based on regional growth also showed its advantages and potentiality. The proposed method is a novel form of semi-automatic road network extraction, and it significantly increases the efficiency of road network extraction.

Effect of dataset distribution on automatic road extraction in very high-resolution orthophoto using DeepLab V3+

“…Road extraction is accelerated semi-automatically by several studies. Kaili Yang arranges extracted pieces of roads one by one and then combines all of them to produce a complete road extraction, where each stage of the work uses various algorithms [9]. Cem proposes to detect the body and shape of the road and then connect them, utilizing graph theory [10].…”