68 citations as recorded by crossref.

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19. Monitoring and Mapping of Shallow Landslides in a Tropical Environment Using Persistent Scatterer Interferometry: A Case Study from the Western Ghats, India A. Rajaneesh et al. https://doi.org/10.3390/geomatics1010002
20. Semi-automatic ranking of landslide candidate areas at a regional scale using EGMS InSAR data for territorial planning and risk management G. Marmoni et al. https://doi.org/10.1007/s10346-025-02597-6
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22. An improved method of three-dimensional displacement field generation in mining areas with a single InSAR pair H. Fan et al. https://doi.org/10.1080/22797254.2019.1654927
23. Monitoring Buildings at Landslide Risk With SAR: A Methodology Based on the Use of Multipass Interferometric Data C. Noviello et al. https://doi.org/10.1109/MGRS.2019.2963140
24. Investigating the kinematics of the unstable slope of Barberà de la Conca (Catalonia, Spain) and the effects on the exposed facilities by GBSAR and multi-source conventional monitoring D. Peduto et al. https://doi.org/10.1007/s10346-020-01500-9
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31. Multi-scale analysis of settlement-induced building damage using damage surveys and DInSAR data: A case study in The Netherlands D. Peduto et al. https://doi.org/10.1016/j.enggeo.2016.12.018
32. Multi-temporal landslide activity investigation by spaceborne SAR interferometry: The case study of the Polish Carpathians K. Pawluszek-Filipiak et al. https://doi.org/10.1016/j.rsase.2021.100629
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35. Assessment of alternative forest road routes and landslide susceptibility mapping using machine learning E. Buğday & A. Akay https://doi.org/10.1590/01047760202228012976
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37. A multi-scale methodological approach for slow-moving landslide risk mitigation in urban areas, southern Italy S. Ferlisi et al. https://doi.org/10.1007/s41207-019-0110-4
38. Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application E. Intrieri et al. https://doi.org/10.5194/nhess-17-1713-2017
39. Quantitative analysis of consequences to masonry buildings interacting with slow-moving landslide mechanisms: a case study D. Peduto et al. https://doi.org/10.1007/s10346-018-1014-0
40. From satellite interferometry displacements to potential damage maps: A tool for risk reduction and urban planning A. Barra et al. https://doi.org/10.1016/j.rse.2022.113294
41. Integration of interpretable machine learning and MT-InSAR for dynamic enhancement of landslide susceptibility in the Three Gorges Reservoir Area F. Zhao et al. https://doi.org/10.1016/j.jrmge.2025.03.043
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44. A multi-disciplinary approach for the damage analysis of cultural heritage: The case study of the St. Gerlando Cathedral in Agrigento D. Reale et al. https://doi.org/10.1016/j.rse.2019.111464
45. 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
46. Retrieval and Prediction of Three-Dimensional Displacements by Combining the DInSAR and Probability Integral Method in a Mining Area C. Zhu et al. https://doi.org/10.1109/JSTARS.2020.2978288
47. Shallow Landslides Physically Based Susceptibility Assessment Improvement Using InSAR. Case Study: Carpathian and Subcarpathian Prahova Valley, Romania I. Armaș et al. https://doi.org/10.3390/rs13122385
48. Geometric and kinematic characterization of landslides affecting urban areas: the Lungro case study (Calabria, Southern Italy) G. Gullà et al. https://doi.org/10.1007/s10346-015-0676-0
49. Analysis and quantification of potential consequences in multirisk coastal context at different spatial scales (Normandy, France) K. Graff et al. https://doi.org/10.1007/s11069-019-03763-5
50. Coupling finite element modelling and InSAR data for enhanced understanding of landslide behaviour along highway infrastructures A. Vitaletti et al. https://doi.org/10.1007/s10346-025-02610-y
51. Landslide detection and characterization using terrestrial 3D laser scanning (LiDAR) M. Ozdogan https://doi.org/10.13168/AGG.2019.0032
52. Empirical fragility and vulnerability curves for buildings exposed to slow-moving landslides at medium and large scales D. Peduto et al. https://doi.org/10.1007/s10346-017-0826-7
53. Detection of buildings with potential damage using differential deformation maps S. Shahbazi et al. https://doi.org/10.1016/j.isprsjprs.2024.10.008
54. A slow-moving landslide risk assessment procedure using thematic maps and InSAR data: a case study C. Dai et al. https://doi.org/10.1080/17499518.2025.2467994
55. Sentinel-1 P-SBAS data for the update of the state of activity of national landslide inventory maps P. Confuorto et al. https://doi.org/10.1007/s10346-022-02024-0
56. Probabilistic analysis of settlement-induced damage to bridges in the city of Amsterdam (The Netherlands) D. Peduto et al. https://doi.org/10.1016/j.trgeo.2018.01.002
57. Multi-Source Data Integration to Investigate a Deep-Seated Landslide Affecting a Bridge J. Pastor et al. https://doi.org/10.3390/rs11161878
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67. Uncertainty pattern in landslide susceptibility prediction modelling: Effects of different landslide boundaries and spatial shape expressions F. Huang et al. https://doi.org/10.1016/j.gsf.2021.101317
68. Quantitative analysis of the risk to road networks exposed to slow-moving landslides: a case study in the Campania region (southern Italy) S. Ferlisi et al. https://doi.org/10.1007/s10346-020-01482-8