35 citations as recorded by crossref.

1. Landslide detection, monitoring and prediction with remote-sensing techniques N. Casagli et al. https://doi.org/10.1038/s43017-022-00373-x
2. 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
3. Development of a Proof-of-Concept A-DInSAR-Based Monitoring Service for Land Subsidence M. Righini et al. https://doi.org/10.3390/rs16111981
4. 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
5. 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
6. Remote sensing for landslide investigations: A progress report from China Q. Xu et al. https://doi.org/10.1016/j.enggeo.2023.107156
7. 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
8. High Performance Computing in Satellite SAR Interferometry: A Critical Perspective P. Imperatore et al. https://doi.org/10.3390/rs13234756
9. 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
10. A statistical-based approach for determining the intensity of unrest phases at Stromboli volcano (Southern Italy) using one-step-ahead forecasts of displacement time series T. Carlà et al. https://doi.org/10.1007/s11069-016-2451-5
11. Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites F. Raspini et al. https://doi.org/10.1038/s41598-018-25369-w
12. Classification of ground deformation using sentinel-1 persistent scatterer interferometry time series S. Mirmazloumi et al. https://doi.org/10.1080/15481603.2022.2030535
13. 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
14. SARClust—A New Tool to Analyze InSAR Displacement Time Series for Structure Monitoring D. Roque et al. https://doi.org/10.3390/su15043728
15. 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
16. Displacement Monitoring and Health Evaluation of Two Bridges Using Sentinel-1 SAR Images Q. Huang et al. https://doi.org/10.3390/rs10111714
17. The mud volcanoes at Santa Barbara and Aragona (Sicily, Italy): a contribution to risk assessment A. Gattuso et al. https://doi.org/10.5194/nhess-21-3407-2021
18. 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
19. 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
20. 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
21. Trend Classification of InSAR Displacement Time Series Using SAE–CNN M. Li et al. https://doi.org/10.3390/rs16010054
22. 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
23. 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
24. Classification of Surface Deformation Using DTW-Transformer Model in Lower Jinsha River Basin M. Liu et al. https://doi.org/10.3390/s26175515
25. 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
26. Localising deformation along the elevation of linear structures: An experiment with space-borne InSAR and RTK GPS on the Roman Aqueducts in Rome, Italy D. Tapete et al. https://doi.org/10.1016/j.apgeog.2015.01.009
27. Review of Satellite Interferometry for Landslide Detection in Italy L. Solari et al. https://doi.org/10.3390/rs12081351
28. 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
29. 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
30. 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
31. 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
32. Automated classification of Persistent Scatterers Interferometry time series M. Berti et al. https://doi.org/10.5194/nhess-13-1945-2013
33. 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
34. 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
35. 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