Detailed hazard identification of urban subsidence in Guangzhou and Foshan by combining InSAR and optical imagery

• Urban subsidence was monitored and analyzed by InSAR and optical images. • An improved model was proposed to obtain optical urban surface change map. • Subsidence causes were automatically interpreted by optical change map and others. • Detailed subsidence hazards were identified by an urbanization-integrated model. Recently Guangzhou and Foshan in China are experiencing significant urbanization and economic development. However, the accelerated urbanization process has contributed significantly to urban land subsidence, causing huge economic losses and endangering safety of infrastructure. This intricate activities on urban surfaces can also lead to pseudo danger in interpreting InSAR-based urban surface deformation, resulting in hazard misidentification in two cities. In order to more accurately identify the hazard of urban surface deformation, we innovatively present a combination of InSAR technology with multi-temporal optical remote sensing data. It can also analyze the specific causes of urban deformation at SAR pixel level in two cities. The SBAS-InSAR method was adopted to obtain an urban subsidence map from 2017 to 2020 based on 110 Sentinel-1 SAR image scenes. To obtain an urban surface change map with a high accuracy, an improved SwiT-UNet++ model was applied based on multi optical Google Earth imagery. By a combined analysis of SAR and optical images, we discovered multiple irregular funnels with subsidence at different scales in both cities, that are mostly relatable to urban surface constructions such as foundation compression, building demolition, and the construction of public facilities. Furthermore, to identify detailed hazard around surface changes, the buffer analysis based on InSAR surface deformation and urban surface change maps was conducted. It revealed the surface deformation signals around certain urban surface change areas are more obvious and pose certain hazard. Finally additional high-risk areas are found in the two cities. By subtracting the optical surface change detection map from the InSAR-based urban subsidence map, the “pseudo danger” caused by urban activities in the interpretation of InSAR-based urban surface deformation is eliminated, enabling precise identification of actual land subsidence hazards. It is realized through a risk assessment experiment in the research area by adding factors of urbanization processes. By combining multiple sources of data and using advanced analytical techniques, we could identify the determining factors contributing to urban subsidence and the detailed hazards and thus, provide valuable information for future urban developments.