33 citations as recorded by crossref.

1. Landslide detection, monitoring and prediction with remote-sensing techniques N. Casagli et al.
2. InterpolatiON of InSAR Time series for the dEtection of ground deforMatiOn eVEnts (ONtheMOVE): application to slow-moving landslides L. Pedretti et al.
3. Development of a Proof-of-Concept A-DInSAR-Based Monitoring Service for Land Subsidence M. Righini et al.
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.
5. A Methodology to Detect and Characterize Uplift Phenomena in Urban Areas Using Sentinel-1 Data R. Bonì et al.
6. Remote sensing for landslide investigations: A progress report from China Q. Xu et al.
7. A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia) M. Rygus et al.
8. High Performance Computing in Satellite SAR Interferometry: A Critical Perspective P. Imperatore et al.
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
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.
11. Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites F. Raspini et al.
12. Classification of ground deformation using sentinel-1 persistent scatterer interferometry time series S. Mirmazloumi et al.
13. Trend Change Point Detection in InSAR Derived Displacement Time Series Using MALkCNN: A Deep Learning Approach S. Arya Fakhri & M. Satari
14. SARClust—A New Tool to Analyze InSAR Displacement Time Series for Structure Monitoring D. Roque et al.
15. A new method to detect changes in displacement rates of slow-moving landslides using InSAR time series A. Urgilez Vinueza et al.
16. Displacement Monitoring and Health Evaluation of Two Bridges Using Sentinel-1 SAR Images Q. Huang et al.
17. The mud volcanoes at Santa Barbara and Aragona (Sicily, Italy): a contribution to risk assessment A. Gattuso et al.
18. A User-Oriented Methodology for DInSAR Time Series Analysis and Interpretation: Landslides and Subsidence Case Studies D. Notti et al.
19. A methodology for ground motion area detection (GMA-D) using A-DInSAR time series in landslide investigations M. Bordoni et al.
20. Persistent Scatterers continuous streaming for landslide monitoring and mapping: the case of the Tuscany region (Italy) F. Raspini et al.
21. Trend Classification of InSAR Displacement Time Series Using SAE–CNN M. Li et al.
22. Automated identification of ground kinematic patterns based on InSAR time series displacement and K-SC clustering Y. Liang et al.
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
24. Recent advancements in multi-temporal methods applied to new generation SAR systems and applications in South America P. Euillades et al.
25. 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.
26. Review of Satellite Interferometry for Landslide Detection in Italy L. Solari et al.
27. Ground Movement Classification Using Statistical Tests Over Persistent Scatterer Interferometry Time Series S. Mirmazloumi et al.
28. The contribution of satellite SAR-derived displacement measurements in landslide risk management practices F. Raspini et al.
29. 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.
30. Automated classification of Persistent Scatterers Interferometry time series M. Berti et al.
31. On the Use of Interferometric Synthetic Aperture Radar Data for Monitoring and Forecasting Natural Hazards M. Bernardi et al.
32. Present-day land subsidence rates, surface faulting hazard and risk in Mexico City with 2014–2020 Sentinel-1 IW InSAR F. Cigna & D. Tapete
33. Identification of Precursors in InSAR Time Series Using Functional Data Analysis Post-Processing: Demonstration on Mud Volcano Eruptions M. Fontana et al.