Our recent paper, published in Remote Sensing of Environment, on subsidence spatial pattern detection has been recognized as an ESI Highly Cited Paper. Multi-temporal InSAR is an effective tool for measuring large-scale land subsidence. However, the measurement points generated by InSAR are too many to be manually analyzed, and automatic subsidence detection and classification methods are still lacking. In this study, we developed an oriented R-CNN deep learning network to automatically detect and classify subsidence bowls using InSAR measurements and multi-source ancillary data.