Machine-Learning-Based Change Detection of Newly Constructed Areas from GF-2 Imagery in Nanjing, China

Abstract

Change detection of the newly constructed areas (NCAs) is important for urban develop-ment. The advances of remote sensing and deep learning algorithms promotes the high precision of the research work. In this study, we firstly constructed a high-resolution labels for change detection based on the GF-2 satellite images, and then applied five deep learning models of change detection, including STANets (BASE, BAM, and PAM), SNUNet (Siam-NestedUNet), and BiT (Bitemporal image Transformer) in the Core Region of Jiangbei New Area of Nanjing, China. The BiT model is based on transformer, and the others are based on CNN (Conventional Neural Network). Experiments have revealed that the STANet-PAM model generally performs the best in detecting the NCAs, and the STANet-PAM model can obtain more detailed information of land changes owing to its pyramid spatial-temporal attention module of multiple scales. At last, we have used the five models to analyze urbanization processes from 2015 to 2021 in the study area. Hopefully, the results of this study could be a momentous reference for urban development planning.