Show source-provided references
1. China Association of Metros (2025, May 01). Statistical Analysis Report on Urban Rail Transit (2024). Available online: https://www.camet.org.cn/xytj/tjxx/660844283682885.shtml.
2. Wang, S., Chen, Z., Zhang, G., Xu, Z., Liu, Y., and Yuan, Y. (2024). Overview and Analysis of Ground Subsidence along China’s Urban Subway Network Based on Synthetic Aperture Radar Interferometry. Remote Sens., 16.
10.3390/rs16091548 ↗3. Wang, Y., Feng, G., Li, Z., Luo, S., Wang, H., Xiong, Z., Zhu, J., and Hu, J. (2022). A Strategy for Variable-Scale InSAR Deformation Monitoring in a Wide Area: A Case Study in the Turpan–Hami Basin, China. Remote Sens., 14.
10.3390/rs14153832 ↗4. Research Progress and Methods of InSAR for Deformation Monitoring
5. Potin, P., Rosich, B., Grimont, P., Miranda, N., Shurmer, I., O’Connell, A., Torres, R., and Krassenburg, M. (2016, January 6–9). Sentinel-1 mission status. Proceedings of the Proceedings of EUSAR 2016: 11th European Conference on Synthetic Aperture Radar, Hamburg, Germany.
6. Geudtner, D., Torres, R., Snoeij, P., and Davidson, M. (2011, January 19–23). Sentinel-1 System Overview. Proceedings of the Fringe, Frascati, Italy.
7. Zhang, J., Ke, C., Shen, X., Lin, J., and Wang, R. (2023). Monitoring Land Subsidence along the Subways in Shanghai on the Basis of Time-Series InSAR. Remote Sens., 15.
10.3390/rs15040908 ↗8. A PSI targets characterization approach to interpreting surface displacement signals: A case study of the Shanghai metro tunnels
10.1016/j.rse.2022.113150 ↗9. Huang, C., Tan, L., Liu, J., and Yan, K. (2024). Multi-Temporal-InSAR Ground Deformation Mapping of Beijing Subway Network Based on ALOS-2 and Sentinel-1 Data. Pol. J. Environ. Stud.
10.15244/pjoes/194592 ↗10. Ge, D., Zhang, L., Li, M., Liu, B., and Wang, Y. (2016, January 10–15). Beijing subway tunnelings and high-speed railway subsidence monitoring with PSInSAR and TerraSAR-X data. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.
10.1109/IGARSS.2016.7730796 ↗11. Wang, H., Feng, G., Xu, B., Yu, Y., Li, Z., Du, Y., and Zhu, J. (2017). Deriving Spatio-Temporal Development of Ground Subsidence Due to Subway Construction and Operation in Delta Regions with PS-InSAR Data: A Case Study in Guangzhou, China. Remote Sens., 9.
10.3390/rs9101004 ↗12. Fadhillah, M.F., Achmad, A.R., and Lee, C.-W. (2020). Integration of InSAR Time-Series Data and GIS to Assess Land Subsidence along Subway Lines in the Seoul Metropolitan Area, South Korea. Remote Sens., 12.
10.3390/rs12213505 ↗14. Monitoring of urban subway lines subsidence Using satellite radar interferometry method (Study area: Part of Tehran Metro Line 7)
10.52547/jgst.12.2.16 ↗15. Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) reveals continued ground deformation in and around Metro Manila, Philippines, associated with groundwater exploitation
10.1007/s11069-022-05509-2 ↗18. Detecting the Deformation Anomalies Induced by Underground Construction Using Multiplatform MT-InSAR: A Case Study in To Kwa Wan Station, Hong Kong
10.1109/JSTARS.2021.3113672 ↗19. Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS
10.1007/s11069-014-1128-1 ↗20. Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions
10.1007/s10064-018-1403-6 ↗21. The Effect of a Subway on the Spatial Distribution of Population
23. (2025, May 01). Housing Construction and Landscape Bureau of Tianhe District, Available online: https://www.thnet.gov.cn/thdt/tzgg/qtgg/content/post_6469073.html.
24. Zhu, W., Zhang, H., Mi, J., Huang, C., Chen, J., and Hu, Y. (2024). Research on the Causes and Prevention of Subsidence of Operating Subway in the Granite Weathering Trough Section. Subgrade Eng., 22–29.
28. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry
10.1109/36.868878 ↗29. Whalen, C.T. (1978). Control Leveling.
30. Luo, X., Liu, G., and Huang, D. (2011, January 26–29). A new approach to accurately estimate deformation in PS-DInSAR: Solution-space search. Proceedings of the International Symposium on Lidar and Radar Mapping 2011: Technologies and Applications, Nanjing, China.
10.1117/12.912330 ↗31. Li, T., Tang, X., Zhou, X., Zhang, X., Li, S., and Gao, X. (2022, January 17–22). Deformation Products Of Lutan-1(Lt-1) Sar Satellite Constellation for Geohazard Monitoring. Proceedings of the IGARSS 2022–2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.
10.1109/IGARSS46834.2022.9883601 ↗32. Twin-Satellite Constellation Design and Realization for Terrain Mapping and Deformation Monitoring: LuTan-1
10.1109/TGRS.2025.3579366 ↗33. Wang, R., Liu, K., Cai, Y., Liang, D., Yu, W., Liu, D., Chen, Y., Yang, Q., Li, J., and Liu, L. (2024, January 23–26). LuTan-1: An Innovative L-band Spaceborne Bistatic Interferometric SAR Mission. Proceedings of the EUSAR 2024; 15th European Conference on Synthetic Aperture Radar, Munich, Germany.
34. Li, T., Tang, X., Zhang, X., Lu, J., and Zhang, X. (2024, January 22–24). Current Status and Prospects of the LuTan-1 Mission. Proceedings of the 2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), Zhuhai, China.
10.1109/ICSIDP62679.2024.10868373 ↗35. Vertical and horizontal displacements of Los Angeles from InSAR and GPS time series analysis: Resolving tectonic and anthropogenic motions
10.1016/j.jog.2016.05.003 ↗36. Enhancing 4-D landslide monitoring and block interaction analysis with a Novel Kalman-filter-based InSAR approach
10.1029/2024JF007923 ↗37. Does transportation infrastructure construction promote population agglomeration? Evidence from 1838 Chinese county-level administrative units
10.1016/j.cities.2023.104409 ↗38. Do Public Transport Improvements Increase Agglomeration Economies? A Review of Literature and an Agenda for Research
10.1080/01441647.2011.587908 ↗39. Exploring the differential impacts of urban transit system on the spatial distribution of local and floating population in Beijing
10.1007/s11442-017-1403-7 ↗40. Expansion of the subway network and spatial distribution of population and employment in the Seoul metropolitan area
10.1177/0042098017725476 ↗43. Satellite-Based InSAR Data-Driven Urban Building Stability Assessment: A Case Study of Kunming City
44. Building risk monitoring and prediction using integrated multi-temporal InSAR and numerical modeling techniques
45. Yang, K., Yan, L., Huang, G., Chen, C., and Wu, Z. (2016). Monitoring building deformation with InSAR: Experiments and validation. Sensors, 16.
10.3390/s16122182 ↗46. Xu, Y., Wu, Z., Zhang, H., Liu, J., and Jing, Z. (2023). Land subsidence monitoring and building risk assessment using InSAR and machine learning in a Loess Plateau City—A case study of Lanzhou, China. Remote Sens., 15.
10.3390/rs15112851 ↗47. (2016). Standard for Dangerous Building Appraisal (Standard No. JGJ 125-2016).
48. (2025, May 01). Guangzhou Water Authority, Available online: https://swj.gz.gov.cn/mssw/szy/szygb/index.html.
49. Sharp, J.M. (2010). The impacts of urbanization on groundwater systems and recharge. AQUA Mundi, 2719.
53. Zhang, X., Cheng, Z., Xu, B., Gui, R., Hu, J., Yang, C., Yang, Q., and Xiong, T. (2023). Coupling the relationship between land subsidence and groundwater level, ground fissures in Xi’an city using multi-orbit and multi-temporal InSAR. Remote Sens., 15.
10.3390/rs15143567 ↗56. Effect of suffosion on deterioration of weathered granite soil: A multi-scale study from macroscopic to microscopic investigations
10.1007/s11440-025-02638-3 ↗