Rethinking BiSeNet: A Lightweight Network for Urban Water Extraction

Abstract

Urban water runs through and plays an important role in urban development, and understanding the spatial changes of urban water bodies is significant. However, the rapid growth of remote sensing data caused by rapid urbanization poses a challenge to the efficiency of water extraction methods. In this paper, a high-precision method with a lightweight structure for urban water extraction was proposed in terms of the dilemma between efficiency and accuracy. We designed SE-ARM, SE-FFM, and DW-ASPP on the basis of BiSeNet to improve it in the water semantic segmentation application, which resulted in SE-BiSeNet. To validate the proposed model’s effectiveness, we compared our SE-BiSeNet to other advanced water extraction methods on the Chengdu dataset in terms of accuracy and efficiency. The results showed that the proposed model performs well in both accuracy (0.96 IoU) and efficiency (6.4 FPS on a GTX 1060 card) with fewer parameters and calculations, which indicates an objective application potential.