12 citations as recorded by crossref.

1. SDCDNet: A Semi-Dual Change Detection Network Framework With Super-Weak Label for Remote Sensing Image J. Wang et al. 10.1109/TGRS.2023.3286113
2. Unsupervised learning framework for region-based damage assessment on xBD, a large satellite imagery P. Mittal et al. 10.1007/s11069-023-06074-y
3. Natural disaster damage analysis using lightweight spatial feature aggregated deep learning model K. Abraham et al. 10.1007/s12145-024-01325-3
4. Deep learning-based automated tile defect detection system for Portuguese cultural heritage buildings N. Karimi et al. 10.1016/j.culher.2024.05.009
5. A Novel UNet 3+ Change Detection Method Considering Scale Uncertainty in High-Resolution Imagery T. Bai et al. 10.3390/rs16111846
6. Advancing Rural Building Extraction via Diverse Dataset Construction and Model Innovation with Attention and Context Learning M. Yu et al. 10.3390/app132413149
7. Assessing the Impact of the 2023 Kahramanmaras Earthquake on Cultural Heritage Sites Using High-Resolution SAR Images C. Boyoğlu et al. 10.3390/heritage6100349
8. InCR: Inception and concatenation residual block-based deep learning network for damaged building detection using remote sensing images B. Tasci et al. 10.1016/j.jag.2023.103483
9. DDFormer: A Dual-Domain Transformer for Building Damage Detection Using High-Resolution SAR Imagery T. Li et al. 10.1109/LGRS.2023.3288007
10. Data collection tools for post-disaster damage assessment of building and lifeline infrastructure systems J. Lozano & I. Tien 10.1016/j.ijdrr.2023.103819
11. Vibration-based building health monitoring using spatio-temporal learning model V. Dang & H. Pham 10.1016/j.engappai.2023.106858
12. Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey J. Gu et al. 10.3390/buildings14040898