85 citations as recorded by crossref.

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2. 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
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5. 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
6. 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
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8. 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
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11. The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings S. Moretto et al. https://doi.org/10.3390/rs13183735
12. Review of Satellite Interferometry for Landslide Detection in Italy L. Solari et al. https://doi.org/10.3390/rs12081351
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17. A Multi-Temporal Small Baseline Interferometry Procedure Applied to Mining-Induced Deformation Monitoring R. Palamà et al. https://doi.org/10.3390/rs14092182
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21. Model-Free Characterization of SAR MTI Time Series A. Refice et al. https://doi.org/10.1109/LGRS.2020.3031655
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23. Multidisciplinary approach for the characterization of a deep-seated landslide in a semi-arid region (Cañón de Yerbabuena, San Luis Potosí, Mexico) C. Villaseñor-Reyes et al. https://doi.org/10.1007/s10346-020-01497-1
24. 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
25. Ground Deformation in The Ciloto Landslides Area Revealed by Multi-Temporal InSAR N. Hayati et al. https://doi.org/10.3390/geosciences10050156
26. 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
27. SARClust—A New Tool to Analyze InSAR Displacement Time Series for Structure Monitoring D. Roque et al. https://doi.org/10.3390/su15043728
28. 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
29. 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
30. 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
31. 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
32. 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
33. 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
34. 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
35. The challenge to use multi-temporal InSAR for landslide early warning M. Schlögl et al. https://doi.org/10.1007/s11069-022-05289-9
36. 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
37. 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
38. 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
39. Identification of Landslide Precursors for Early Warning of Hazards with Remote Sensing K. Strząbała et al. https://doi.org/10.3390/rs16152781
40. 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
41. 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
42. 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
43. 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
44. 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
45. 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
46. 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
47. 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
48. Automatic Mapping of Potential Landslides Using Satellite Multitemporal Interferometry Y. Zhang et al. https://doi.org/10.3390/rs15204951
49. Statistically-Based Trend Analysis of MTInSAR Displacement Time Series F. Bovenga et al. https://doi.org/10.3390/rs13122302
50. 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
51. 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
52. 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
53. 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
54. 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
55. 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
56. 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
57. 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
58. 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
59. Analysis of InSAR displacements for the slopes around Odelouca reservoir D. Roque et al. https://doi.org/10.1016/j.procs.2018.10.048
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61. Offline-Online Change Detection for Sentinel-1 InSAR Time Series E. Hussain et al. https://doi.org/10.3390/rs13091656
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64. Multi-Sensor Satellite Analysis for Landslide Characterization: A Case of Study from Baipaza, Tajikistan F. Poggi et al. https://doi.org/10.3390/rs17122003
65. 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
66. Exploring the InSAR Deformation Series Using Unsupervised Learning in a Built Environment M. Yang et al. https://doi.org/10.3390/rs16081375
67. 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
68. 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
69. PSDefoPAT—Persistent Scatterer Deformation Pattern Analysis Tool M. Evers et al. https://doi.org/10.3390/rs15194646
70. 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
71. Monitoring reclaimed lands subsidence in Hong Kong with InSAR technique by persistent and distributed scatterers M. Wang et al. https://doi.org/10.1007/s11069-016-2196-1
72. Landslide detection, monitoring and prediction with remote-sensing techniques N. Casagli et al. https://doi.org/10.1038/s43017-022-00373-x
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78. 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
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82. 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
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