92 citations as recorded by crossref.

1. Natural and Anthropogenic Geohazards in Greater London Observed from Geological and ERS-1/2 and ENVISAT Persistent Scatterers Ground Motion Data: Results from the EC FP7-SPACE PanGeo Project F. Cigna et al. https://doi.org/10.1007/s00024-014-0927-3
2. Hybrid deep learning for automated detection of spatiotemporal change points in InSAR time series: a case study on the Campi Flegrei volcano using a CNN–LSTM model S. Fakhri & M. Satari https://doi.org/10.1007/s00531-026-02572-z
3. Trend Change Point Detection in InSAR Derived Displacement Time Series Using MALkCNN: A Deep Learning Approach S. Arya Fakhri & M. Satari https://doi.org/10.1007/s41064-025-00342-1
4. The simulation of urban development with the consideration of ground deformation threats in Sidoarjo Regency, East Java Province of Indonesia A. Hadianti & B. Dewanto https://doi.org/10.1016/j.rsase.2023.101019
5. The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings S. Moretto et al. https://doi.org/10.3390/rs13183735
6. Review of Satellite Interferometry for Landslide Detection in Italy L. Solari et al. https://doi.org/10.3390/rs12081351
7. Trend Classification of InSAR Displacement Time Series Using SAE–CNN M. Li et al. https://doi.org/10.3390/rs16010054
8. Unsupervised detection of InSAR time series patterns based on PCA and K-means clustering D. Festa et al. https://doi.org/10.1016/j.jag.2023.103276
9. Landslide prediction, monitoring and early warning: a concise review of state-of-the-art B. Chae et al. https://doi.org/10.1007/s12303-017-0034-4
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11. Unsupervised Machine Learning for Dynamic Slope Stability Classification: A Comparative Evaluation of PCA-K-Means, SOM, and Hybrid Algorithms Using InSAR Time-Series Data D. Owusu-Ansah et al. https://doi.org/10.3390/app16115577
12. On the Use of Interferometric Synthetic Aperture Radar Data for Monitoring and Forecasting Natural Hazards M. Bernardi et al. https://doi.org/10.1007/s11004-021-09948-8
13. Recent advancements in multi-temporal methods applied to new generation SAR systems and applications in South America P. Euillades et al. https://doi.org/10.1016/j.jsames.2021.103410
14. Classification of Surface Deformation Using DTW-Transformer Model in Lower Jinsha River Basin M. Liu et al. https://doi.org/10.3390/s26175515
15. The role of imaging radar in cultural heritage: From technologies to applications F. Chen et al. https://doi.org/10.1016/j.jag.2022.102907
16. Synergistic Use of Synthetic Aperture Radar Interferometry and Geomorphological Analysis in Slow-Moving Landslide Investigation in the Northern Apennines (Italy) C. Parenti et al. https://doi.org/10.3390/land13091505
17. The challenge to use multi-temporal InSAR for landslide early warning M. Schlögl et al. https://doi.org/10.1007/s11069-022-05289-9
18. Advanced SAR Interferometric Analysis to Support Geomorphological Interpretation of Slow-Moving Coastal Landslides (Malta, Mediterranean Sea) M. Mantovani et al. https://doi.org/10.3390/rs8060443
19. Semiautomatic analysis of EGMS InSAR data for geohazards detection: a study on Lazio Region (Italy) through ADATools C. Medici et al. https://doi.org/10.1080/19475705.2025.2582755
20. Ground Stability Monitoring of Undermined and Landslide Prone Areas by Means of Sentinel-1 Multi-Temporal InSAR, Case Study from Slovakia R. Czikhardt et al. https://doi.org/10.3390/geosciences7030087
21. Building health monitoring in the old town of Madrid: applicability of SAR Imagery to the monitoring of underground works through classification indexes A. Garcia et al. https://doi.org/10.1080/17538947.2020.1815878
22. Complex Surface Deformation of the Coalfield in the Northwest Suburbs of Xuzhou from 2015 to 2020 Revealed by Time Series InSAR L. Li et al. https://doi.org/10.1080/07038992.2021.1951190
23. Automatic Mapping of Potential Landslides Using Satellite Multitemporal Interferometry Y. Zhang et al. https://doi.org/10.3390/rs15204951
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25. Investigation of a Small Landslide in the Qinghai-Tibet Plateau by InSAR and Absolute Deformation Model J. Hao et al. https://doi.org/10.3390/rs11182126
26. A new method to detect changes in displacement rates of slow-moving landslides using InSAR time series A. Urgilez Vinueza et al. https://doi.org/10.1007/s10346-022-01913-8
27. The relationship between intermittent coherence and precision of ISBAS InSAR ground motion velocities: ERS-1/2 case studies in the UK F. Cigna & A. Sowter https://doi.org/10.1016/j.rse.2017.05.016
28. Tunnel-induced land subsidence assessment in a densely populated residential area using Sentinel-1 PS-InSAR A. Karunathilake et al. https://doi.org/10.1007/s44288-024-00086-2
29. Satellite InSAR survey of structurally-controlled land subsidence due to groundwater exploitation in the Aguascalientes Valley, Mexico F. Cigna & D. Tapete https://doi.org/10.1016/j.rse.2020.112254
30. Automatic landslide prioritization at regional scale through PS-InSAR cluster analysis and socio-economic impacts M. Zocchi et al. https://doi.org/10.1016/j.rsase.2024.101414
31. Offline-Online Change Detection for Sentinel-1 InSAR Time Series E. Hussain et al. https://doi.org/10.3390/rs13091656
32. Multi-Sensor Satellite Analysis for Landslide Characterization: A Case of Study from Baipaza, Tajikistan F. Poggi et al. https://doi.org/10.3390/rs17122003
33. A clustering-guided statistical inference workflow for large-area TS-InSAR deformation interpretation: A case study in the Menyuan permafrost region H. Zhou et al. https://doi.org/10.1016/j.rsase.2026.102273
34. Deformation responses of slow moving landslides to seasonal rainfall in the Northern Apennines, measured by InSAR B. Bayer et al. https://doi.org/10.1016/j.geomorph.2018.02.020
35. Deformation Pattern Classification of Sea-Crossing Bridge InSAR Time Series Based on a Transfer Learning Framework L. Ren et al. https://doi.org/10.3390/rs17213567
36. Classification of Urban Land Subsidence Types in Fuzhou from Time-Series InSAR Using FFT-Based Filtering and Ensemble Learning Z. Zhao et al. https://doi.org/10.3390/rs18162778
37. Supervised Machine Learning Algorithms for Ground Motion Time Series Classification from InSAR Data S. Mirmazloumi et al. https://doi.org/10.3390/rs14153821
38. Rating health and stability of engineering structures via classification indexes of InSAR Persistent Scatterers F. Pratesi et al. https://doi.org/10.1016/j.jag.2015.04.012
39. A Transfer Learning Approach for Deformation Pattern Recognition in InSAR Time Series M. Yang et al. https://doi.org/10.1109/TGRS.2025.3543580
40. A User-Oriented Methodology for DInSAR Time Series Analysis and Interpretation: Landslides and Subsidence Case Studies D. Notti et al. https://doi.org/10.1007/s00024-015-1071-4
41. A Robust Attention-Based Time-Gated LSTM for Change Point Detection in Challenging InSAR Time Series with Data Gaps S. Fakhri & M. Satari https://doi.org/10.1007/s41064-026-00401-1
42. Ground motion baseline analysis of the Cheshire UK GeoEnergy Observatory A. Novellino et al. https://doi.org/10.1038/s41598-021-95191-4
43. remotIO: A Sentinel-1 Multi-Temporal InSAR Infrastructure Monitoring Service with Automatic Updates and Data Mining Capabilities M. Bakon et al. https://doi.org/10.3390/rs12111892
44. Neural Network Pattern Recognition Experiments Toward a Fully Automatic Detection of Anomalies in InSAR Time Series of Surface Deformation P. Milillo et al. https://doi.org/10.3389/feart.2021.728643
45. Automated Time-Series InSAR Monitoring and Failure Prediction of Active Landslides: A Case Study of the Xinmo Landslide, China H. Li et al. https://doi.org/10.1109/JSTARS.2026.3662761
46. Innovative analysis and use of high‐resolution DTMs for quantitative interrogation of Earth‐surface processes P. Passalacqua et al. https://doi.org/10.1002/esp.3616
47. Change in regional land subsidence in Beijing after south-to-north water diversion project observed using satellite radar interferometry M. Lyu et al. https://doi.org/10.1080/15481603.2019.1676973
48. Sentinel-1 Big Data Processing with P-SBAS InSAR in the Geohazards Exploitation Platform: An Experiment on Coastal Land Subsidence and Landslides in Italy F. Cigna & D. Tapete https://doi.org/10.3390/rs13050885
49. Assessing the Potential of Long, Multi-Temporal SAR Interferometry Time Series for Slope Instability Monitoring: Two Case Studies in Southern Italy F. Bovenga et al. https://doi.org/10.3390/rs14071677
50. Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning N. Casagli et al. https://doi.org/10.1186/s40677-017-0073-1
51. Persistent Scatterers continuous streaming for landslide monitoring and mapping: the case of the Tuscany region (Italy) F. Raspini et al. https://doi.org/10.1007/s10346-019-01249-w
52. A Multi-Temporal Small Baseline Interferometry Procedure Applied to Mining-Induced Deformation Monitoring R. Palamà et al. https://doi.org/10.3390/rs14092182
53. PSI-based methodology to land subsidence mechanism recognition R. Bonì et al. https://doi.org/10.5194/piahs-372-357-2015
54. Model-Free Characterization of SAR MTI Time Series A. Refice et al. https://doi.org/10.1109/LGRS.2020.3031655
55. Classification of ground deformation using sentinel-1 persistent scatterer interferometry time series S. Mirmazloumi et al. https://doi.org/10.1080/15481603.2022.2030535
56. Ground Deformation in The Ciloto Landslides Area Revealed by Multi-Temporal InSAR N. Hayati et al. https://doi.org/10.3390/geosciences10050156
57. Kinematic Mapping and Geomorphological Analysis of Rock Glaciers in the Pirin Mountains (Bulgaria) F. Sîrbu et al. https://doi.org/10.3390/rs18162754
58. SARClust—A New Tool to Analyze InSAR Displacement Time Series for Structure Monitoring D. Roque et al. https://doi.org/10.3390/su15043728
59. TS-DInSAR tool: a temporal & spatial tool for the analysis of DInSAR data at the national scale F. Poggi et al. https://doi.org/10.1080/15481603.2026.2700111
60. InterpolatiON of InSAR Time series for the dEtection of ground deforMatiOn eVEnts (ONtheMOVE): application to slow-moving landslides L. Pedretti et al. https://doi.org/10.1007/s10346-023-02073-z
61. Dam Deformation Monitoring using Cloud-Based P-SBAS Algorithm: The Kramis Dam Case (Algeria) K. Hasni et al. https://doi.org/10.48084/etasr.5857
62. Deceleration captured by InSAR after local stabilization works in a slow-moving landslide: the case of Arcos de la Frontera (SW Spain) G. Bru et al. https://doi.org/10.1007/s10346-024-02292-y
63. Ground Movement Classification Using Statistical Tests Over Persistent Scatterer Interferometry Time Series S. Mirmazloumi et al. https://doi.org/10.1016/j.procs.2021.11.068
64. A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia) M. Rygus et al. https://doi.org/10.3390/rs15153776
65. Identification of Precursors in InSAR Time Series Using Functional Data Analysis Post-Processing: Demonstration on Mud Volcano Eruptions M. Fontana et al. https://doi.org/10.3390/rs16071191
66. Identification of Landslide Precursors for Early Warning of Hazards with Remote Sensing K. Strząbała et al. https://doi.org/10.3390/rs16152781
67. Unsupervised Clustering of InSAR Time-Series Deformation in Mandalay Region from 2022 to 2025 Using Dynamic Time Warping and Longest Common Subsequence J. Qin et al. https://doi.org/10.3390/rs17233920
68. Copernicus Sentinel-1 MT-InSAR, GNSS and Seismic Monitoring of Deformation Patterns and Trends at the Methana Volcano, Greece T. Gatsios et al. https://doi.org/10.3390/app10186445
69. The contribution of satellite SAR-derived displacement measurements in landslide risk management practices F. Raspini et al. https://doi.org/10.1007/s11069-016-2691-4
70. InSAR phase gradient reveals fault‐zone controls on the spatial distribution of slow‐moving landslides in the active orogenic region of Hazara‐Kashmir, Pakistan S. Ahmad et al. https://doi.org/10.1002/esp.6015
71. Using advanced InSAR techniques to monitor landslide deformations induced by tunneling in the Northern Apennines, Italy B. Bayer et al. https://doi.org/10.1016/j.enggeo.2017.03.026
72. Automated identification of ground kinematic patterns based on InSAR time series displacement and K-SC clustering Y. Liang et al. https://doi.org/10.1016/j.enggeo.2025.108367
73. Kinematic pattern interpretation of railway-corridor deformation from InSAR time series with transfer learning-based TCN M. Yang et al. https://doi.org/10.1016/j.jag.2026.105594
74. PSInSAR Analysis in the Pisa Urban Area (Italy): A Case Study of Subsidence Related to Stratigraphical Factors and Urbanization L. Solari et al. https://doi.org/10.3390/rs8020120
75. Analysis of InSAR displacements for the slopes around Odelouca reservoir D. Roque et al. https://doi.org/10.1016/j.procs.2018.10.048
76. Improving Ground Deformation Classification by Integrating InSAR Time Series With Geospatial Information Y. Dong et al. https://doi.org/10.1109/TGRS.2025.3635271
77. Statistical and Independent Component Analysis of Sentinel-1 InSAR Time Series to Assess Land Subsidence Trends C. Farías et al. https://doi.org/10.3390/rs16214066
78. From ERS-1/2 to Sentinel-1: two decades of subsidence monitored through A-DInSAR techniques in the Ravenna area (Italy) S. Fiaschi et al. https://doi.org/10.1080/15481603.2016.1269404
79. A Data Mining Approach for Multivariate Outlier Detection in Postprocessing of Multitemporal InSAR Results M. Bakon et al. https://doi.org/10.1109/JSTARS.2017.2686646
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81. Site scale landslide deformation and strain analysis using MT-InSAR and GNSS approach – A case study V. Kumar Maurya et al. https://doi.org/10.1016/j.asr.2022.05.028
82. PSDefoPAT—Persistent Scatterer Deformation Pattern Analysis Tool M. Evers et al. https://doi.org/10.3390/rs15194646
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85. Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites F. Raspini et al. https://doi.org/10.1038/s41598-018-25369-w
86. Reactivation of an old landslide in north–central Iran following reservoir impoundment: Results from multisensor satellite time-series analysis M. Vassileva et al. https://doi.org/10.1016/j.enggeo.2023.107337
87. Methodology for Detection and Interpretation of Ground Motion Areas with the A-DInSAR Time Series Analysis R. Bonì et al. https://doi.org/10.3390/rs8080686
88. A methodology for ground motion area detection (GMA-D) using A-DInSAR time series in landslide investigations M. Bordoni et al. https://doi.org/10.1016/j.catena.2017.12.013
89. A Methodology to Detect and Characterize Uplift Phenomena in Urban Areas Using Sentinel-1 Data R. Bonì et al. https://doi.org/10.3390/rs10040607
90. Present-day land subsidence rates, surface faulting hazard and risk in Mexico City with 2014–2020 Sentinel-1 IW InSAR F. Cigna & D. Tapete https://doi.org/10.1016/j.rse.2020.112161
91. A bridge-tailored multi-temporal DInSAR approach for remote exploration of deformation characteristics and mechanisms of complexly structured bridges X. Qin et al. https://doi.org/10.1016/j.isprsjprs.2019.08.003
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