35 citations as recorded by crossref.

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2. Automatic detection of seismic damages from very high monoscopic spatial images M. Bensaibi et al. https://doi.org/10.1007/s42107-020-00260-0
3. Tracking surface and subsurface deformation associated with groundwater dynamics following the 2019 Mirpur earthquake M. Khan et al. https://doi.org/10.1080/19475705.2023.2195966
4. Seismic site characterization and nonlinear ground response analysis of Islamabad, Pakistan M. Yasir et al. https://doi.org/10.1007/s10950-026-10458-5
5. Earthquake building damage classification based on full suite of Sentinel-1 features X. Lv et al. https://doi.org/10.1515/geo-2025-0882
6. 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
7. Building-damage detection using pre- and post-seismic high-resolution satellite stereo imagery: A case study of the May 2008 Wenchuan earthquake X. Tong et al. https://doi.org/10.1016/j.isprsjprs.2011.12.004
8. 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
9. 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
10. 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
11. Earthquake-induced damage scenario simulation S. Sharma & B. Rastogi https://doi.org/10.1016/j.rsase.2021.100585
12. A Soft Computing Method for Damage Mapping Using VHR Optical Satellite Imagery B. Mansouri & Y. Hamednia https://doi.org/10.1109/JSTARS.2015.2493342
13. 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
14. 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
15. 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
16. Semiautomated object-based classification of rain-induced landslides with VHR multispectral images on Madeira Island S. Heleno et al. https://doi.org/10.5194/nhess-16-1035-2016
17. An approach for detection of buildings and changes in buildings using orthophotos and point clouds: A case study of Van Erriş earthquake G. Sarp et al. https://doi.org/10.5721/EuJRS20144735
18. Transfer Learning for Improving Seismic Building Damage Assessment Q. Lin et al. https://doi.org/10.3390/rs14010201
19. The 2011 Tohoku (Japan) Tsunami Inundation and Liquefaction Investigated Through Optical, Thermal, and SAR Data M. Chini et al. https://doi.org/10.1109/LGRS.2012.2205661
20. 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
21. 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
22. 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
23. Segment-by-segment comparison technique for earthquake-induced building damage map generation using satellite imagery N. Khodaverdizahraee et al. https://doi.org/10.1016/j.ijdrr.2020.101505
24. 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
25. 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
26. A Review of Deep Learning Methodologies for Building Damage Assessment Using Optical Satellite Imagery H. Tan et al. https://doi.org/10.1109/ACCESS.2026.3702619
27. Earthquake damage mapping: An overall assessment of ground surveys and VHR image change detection after L'Aquila 2009 earthquake R. Anniballe et al. https://doi.org/10.1016/j.rse.2018.03.004
28. Classification of Building Damage Triggered by Earthquakes Using Decision Tree S. Li & H. Tang https://doi.org/10.1155/2020/2930515
29. 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
30. 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
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32. 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
33. 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
34. 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
35. 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