67 citations as recorded by crossref.

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22. 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
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30. 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
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34. 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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38. 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
39. 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
40. 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
41. Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting A. van Natijne et al. https://doi.org/10.3390/s20051425
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43. 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
44. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. Landslide detection and characterization using terrestrial 3D laser scanning (LiDAR) M. Ozdogan https://doi.org/10.13168/AGG.2019.0032
51. 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
52. Detection of buildings with potential damage using differential deformation maps S. Shahbazi et al. https://doi.org/10.1016/j.isprsjprs.2024.10.008
53. 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
54. 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
55. 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
56. 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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60. From ERS 1/2 to Sentinel-1: Subsidence Monitoring in Italy in the Last Two Decades L. Solari et al. https://doi.org/10.3389/feart.2018.00149
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67. 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