33 citations as recorded by crossref.

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4. Earthquake building damage classification based on full suite of Sentinel-1 features X. Lv et al. https://doi.org/10.1515/geo-2025-0882
5. On the Generalization Ability of a Global Model for Rapid Building Mapping from Heterogeneous Satellite Images of Multiple Natural Disaster Scenarios Y. Hu & H. Tang https://doi.org/10.3390/rs13050984
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7. High-resolution landslide mapping and susceptibility assessment: Landslide temporal variations and vegetation recovery M. Ali et al. https://doi.org/10.1016/j.asr.2024.06.048
8. A review of landslides related to the 2005 Kashmir Earthquake: implication and future challenges M. Basharat et al. https://doi.org/10.1007/s11069-021-04688-8
9. Earthquake Damage Visualization (EDV) Technique for the Rapid Detection of Earthquake-Induced Damages Using SAR Data R. Sharma et al. https://doi.org/10.3390/s17020235
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12. Anomalous co-seismic surface effects produced by the 2014 Mw 6.2 Ludian earthquake, Yunnan, China: An example of complex faulting related to Riedel shear structures J. Luo et al. https://doi.org/10.1016/j.enggeo.2020.105476
13. Estimating earthquake-damage areas using Landsat-8 OLI surface reflectance data X. Fan et al. https://doi.org/10.1016/j.ijdrr.2018.10.013
14. Extraction of Earthquake-Induced Collapsed Buildings From Bi-Temporal VHR Images Using Object-Level Homogeneity Index and Histogram J. Liu & P. Li https://doi.org/10.1109/JSTARS.2019.2904670
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19. A comprehensive review of earthquake-induced building damage detection with remote sensing techniques L. Dong & J. Shan https://doi.org/10.1016/j.isprsjprs.2013.06.011
20. Machine learning in earthquake- and typhoon-triggered landslide susceptibility mapping and critical factor identification M. Ali et al. https://doi.org/10.1007/s12665-021-09510-z
21. Revolutionizing building damage detection: A novel weakly supervised approach using high-resolution remote sensing images W. Qiao et al. https://doi.org/10.1080/17538947.2023.2298245
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23. A review of the 2005 Kashmir earthquake-induced landslides; from a remote sensing prospective M. Shafique et al. https://doi.org/10.1016/j.jseaes.2016.01.002
24. A Precision Efficient Method for Collapsed Building Detection in Post-Earthquake UAV Images Based on the Improved NMS Algorithm and Faster R-CNN J. Ding et al. https://doi.org/10.3390/rs14030663
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27. A Comprehensive Review of Geospatial Technology Applications in Earthquake Preparedness, Emergency Management, and Damage Assessment M. Shafapourtehrany et al. https://doi.org/10.3390/rs15071939
28. Damage and geological assessment of the 18 September 2011 Mw 6.9 earthquake in Sikkim, India using very high resolution satellite data T. Martha et al. https://doi.org/10.1016/j.gsf.2013.12.011
29. Geotechnical investigation and landslide susceptibility assessment along the Neelum road: a case study from Lesser Himalayas, Pakistan K. Ahmed et al. https://doi.org/10.1007/s12517-021-07396-6
30. Scale Object Selection (SOS) through a hierarchical segmentation by a multi-spectral per-pixel classification M. Chini et al. https://doi.org/10.1016/j.patrec.2014.07.012
31. A 3D Shape Descriptor Based on Contour Clusters for Damaged Roof Detection Using Airborne LiDAR Point Clouds M. He et al. https://doi.org/10.3390/rs8030189
32. Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning N. Casagli et al. https://doi.org/10.1186/s40677-017-0073-1
33. Incorporating multi-source remote sensing in the detection of earthquake-damaged buildings based on logistic regression modelling Q. Li et al. https://doi.org/10.1016/j.heliyon.2024.e32851