BUILDING DAMAGE ASSESSMENT WITH DEEP LEARNING

6 citations as recorded by crossref.

1. A Novel Weighted Ensemble Transferred U-Net Based Model (WETUM) for Postearthquake Building Damage Assessment From UAV Data: A Comparison of Deep Learning- and Machine Learning-Based Approaches E. Khankeshizadeh et al. https://doi.org/10.1109/TGRS.2024.3354737
2. Rapid automatic detection of collapsed buildings with single period LiDAR data after an earthquake Ö. Canözü & H. Acar https://doi.org/10.1007/s12145-024-01659-y
3. Earthquake building damage classification based on full suite of Sentinel-1 features X. Lv et al. https://doi.org/10.1515/geo-2025-0882
4. Automatic classification of buildings using post-earthquake single-epoch photogrammetric 3D point cloud data for rapid assessment: a case study of the February 6, 2023 Kahramanmaraş earthquakes in Türkiye H. Acar & Ö. Canözü https://doi.org/10.1007/s13349-026-01143-9
5. Deep Learning Models for Hazard-Damaged Building Detection Using Remote Sensing Datasets: A Comprehensive Review L. Wang et al. https://doi.org/10.1109/JSTARS.2024.3449097
6. DamageCAT: A deep learning transformer framework for typology-based post-disaster building damage categorization Y. Xiao & A. Mostafavi https://doi.org/10.1016/j.ijdrr.2025.105704