Building roof extraction from ASTIL echo images applying OSA-YOLOv5s

Abstract

Light detection and ranging (LiDAR) is a type of essential tool for urban planning and geoinformation extraction. Airborne streak tube imaging LiDAR (ASTIL) is a new system with great advantages in the rapid collection of remote sensing data. To the best of our knowledge, a new method to extract a building roof from the echo images of ASTIL is proposed. We improve YOLOv5s with a one-shot aggregation (OSA) module to improve efficiency. The experimental results show that the mean average precision of the OSA-YOLOv5s algorithm can reach 95.2%, and the frames per second can reach 11.74 using a CPU and 39.39 using a GPU. The method proposed can extract building objects efficiently from the echo images of ASTIL and acquire the building roof point cloud.