Automatic Detection of Widely Distributed Local-Scale Subsidence Bowls in Rapidly Urbanizing Metropolitan Region Using Time-Series InSAR and Deep Learning Methods

Abstract

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) has been used to produce deformation velocity map for investigating the surface subsidence in the rapidly urbanizing metropolitan regions. However, simple analysis techniques like thresholding cannot detect and locate the widely distributed local-scale subsidence reliably. In this study, we propose a deep-learning based method to automatically detect the local-scale subsidence bowls in the deformation velocity map. To test our method, we choose the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as the study region, where widespread local-scale subsidence bowls exist associated with the urbanization. Using deformation velocity maps spanning 2015–2017 derived from MT-InSAR, our method detects several subsidence bowls due to dewatering, excavation of foundation pits and subways, and other engineering works. The results demonstrate the potential applicability of the proposed method to automatically detect and analyze the local-scale subsidence bowls in the built-up regions.