DEEP LEARNING FOR AUTOMATIC BUILDING DAMAGE ASSESSMENT: APPLICATION IN POST-DISASTER SCENARIOS USING UAV DATA

20 citations as recorded by crossref.

1. Earthquake‐induced building damage recognition from unmanned aerial vehicle remote sensing using scale‐invariant feature transform characteristics and support vector machine classification Y. Zhang et al. https://doi.org/10.1177/87552930231157549
2. Automatic Detection of Collapsed Buildings after the 6 February 2023 Türkiye Earthquakes Using Post-Disaster Satellite Images with Deep Learning-Based Semantic Segmentation Models K. Hacıefendioğlu et al. https://doi.org/10.3390/buildings14030582
3. EMYNet-BDD: EfficientViTB Meets Yolov8 in the Encoder–Decoder Architecture for Building Damage Detection Using Postevent Remote Sensing Images M. Gomroki et al. https://doi.org/10.1109/JSTARS.2024.3427017
4. Advanced Digital Technologies in the Post-Disaster Reconstruction Process—A Review Leveraging Small Language Models A. Rawat et al. https://doi.org/10.3390/buildings14113367
5. Post Disaster Damage Assessment Using Ultra-High-Resolution Aerial Imagery with Semi-Supervised Transformers D. Singh & V. Hoskere https://doi.org/10.3390/s23198235
6. Residential Wildfire Structural Damage Detection Using Deep Learning to Analyze Uncrewed Aerial System (UAS) Imagery, Aerial Imagery, and Satellite Imagery D. Kang et al. https://doi.org/10.1002/fam.3282
7. Monitoring global development aid with machine learning M. Toetzke et al. https://doi.org/10.1038/s41893-022-00874-z
8. Semantic-Guided Multiview Stereo Reconstruction for Aerial Image W. Zhang et al. https://doi.org/10.1109/TGRS.2025.3585623
9. Earthquake building damage classification based on full suite of Sentinel-1 features X. Lv et al. https://doi.org/10.1515/geo-2025-0882
10. Mapping housing stock characteristics from drone images for climate resilience in the Caribbean I. Tingzon et al. https://doi.org/10.1017/eds.2024.46
11. Rapid Post-earthquake Damage Detection Using UAV Imagery and YOLO: A Case Study from the Kahramanmaras Earthquake M. Bayraktar et al. https://doi.org/10.1007/s12524-026-02548-1
12. Application of Drone Images Analysis to Predict the Damage of Streets in HCMC H. Ngoc https://doi.org/10.1109/ACCESS.2025.3610426
13. Methodology for Object-Level Change Detection in Post-Earthquake Building Damage Assessment Based on Remote Sensing Images: OCD-BDA Z. Xie et al. https://doi.org/10.3390/rs16224263
14. Widened Attention‐Enhanced Atrous Convolutional Network for Efficient Embedded Vision Applications under Resource Constraints M. Ferdaus et al. https://doi.org/10.1002/aisy.202300480
15. Application of YOLO and U-Net models for building material identification on segmented images R. Voronkov & M. Bezuglyi https://doi.org/10.35784/iapgos.6968
16. Implementation of remote-sensing models to identify post-disaster health facility damage: Comparative approaches to the 2023 earthquake in Turkey A. Ramachandran et al. https://doi.org/10.1371/journal.pdig.0001060
17. SDL-MVS: View Space and Depth Deformable Learning Paradigm for Multiview Stereo Reconstruction in Remote Sensing Y. Mao et al. https://doi.org/10.1109/TGRS.2024.3464574
18. A Rapid Self-Supervised Deep-Learning-Based Method for Post-Earthquake Damage Detection Using UAV Data (Case Study: Sarpol-e Zahab, Iran) N. Takhtkeshha et al. https://doi.org/10.3390/rs15010123
19. Image classification on Post-Earthquake damage assessment: A case of the 2023 Kahramanmaraş earthquake G. Özerol Özman et al. https://doi.org/10.1016/j.jestch.2024.101780
20. A Simplified Procedure for the Out-of-Plane Seismic Assessment of Free-Standing URM Elements: Application to the Abandoned Ancient Village of Bussana (Sanremo, Imperia - Italy) S. Degli Abbati et al. https://doi.org/10.1080/15583058.2024.2375036