SATELLITE IMAGE CLASSIFICATION OF BUILDING DAMAGES USING AIRBORNE AND SATELLITE IMAGE SAMPLES IN A DEEP LEARNING APPROACH

48 citations as recorded by crossref.

1. Typical advances of artificial intelligence in civil engineering Y. Xu et al. https://doi.org/10.1177/13694332221127340
2. Rapid Fine-Grained Damage Assessment of Buildings on a Large Scale: A Case Study of the February 2023 Earthquake in Turkey Z. Hong et al. https://doi.org/10.1109/JSTARS.2024.3362809
3. Hurricane Damage Detection From Satellite Imagery Using Convolutional Neural Networks S. Kaur et al. https://doi.org/10.4018/IJISMD.306637
4. Data-Driven Approaches for Tornado Damage Estimation with Unpiloted Aerial Systems Z. Chen et al. https://doi.org/10.3390/rs13091669
5. On Transfer Learning for Building Damage Assessment from Satellite Imagery in Emergency Contexts I. Bouchard et al. https://doi.org/10.3390/rs14112532
6. Post-Disaster Building Database Updating Using Automated Deep Learning: An Integration of Pre-Disaster OpenStreetMap and Multi-Temporal Satellite Data S. Ghaffarian et al. https://doi.org/10.3390/rs11202427
7. Change-centric building damage assessment across multiple disasters using deep learning A. Asif et al. https://doi.org/10.1007/s41060-024-00577-y
8. Detection of Disaster-Affected Cultural Heritage Sites from Social Media Images Using Deep Learning Techniques P. Kumar et al. https://doi.org/10.1145/3383314
9. Damage assessment with global differences and local attention 杰. 梅 & 明. 程 https://doi.org/10.1360/SSI-2021-0384
10. RETRACTED ARTICLE: Convolutional neural network based hurricane damage detection using satellite images S. Kaur et al. https://doi.org/10.1007/s00500-022-06805-6
11. An Integrated Parallel Inner Deep Learning Models Information Fusion With Bayesian Optimization for Land Scene Classification in Satellite Images A. Hamza et al. https://doi.org/10.1109/JSTARS.2023.3324494
12. D2ANet: Difference-aware attention network for multi-level change detection from satellite imagery J. Mei et al. https://doi.org/10.1007/s41095-022-0325-1
13. Aerial Image Classification in Post Flood Scenarios Using Robust Deep Learning and Explainable Artificial Intelligence A. Manaf et al. https://doi.org/10.1109/ACCESS.2025.3543078
14. Vision-based multi-level synthetical evaluation of seismic damage for RC structural components: a multi-task learning approach Y. Xu et al. https://doi.org/10.1007/s11803-023-2153-4
15. Extracting building footprints from UAV imagery for urban digital transformation in Ho Chi Minh City T. Trang et al. https://doi.org/10.11628/ksppe.2026.29.1.145
16. Damage-augmented digital twins towards the automated inspection of buildings B. Pantoja-Rosero et al. https://doi.org/10.1016/j.autcon.2023.104842
17. Remote Sensing Building Damage Assessment Based on Machine Learning J. Tang et al. https://doi.org/10.2478/ijanmc-2024-0021
18. Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review S. Matin & B. Pradhan https://doi.org/10.1080/10106049.2021.1933213
19. Building change detection using the parallel spatial-channel attention block and edge-guided deep network A. Eftekhari et al. https://doi.org/10.1016/j.jag.2023.103180
20. Automatic vehicle identification from Google Earth satellite images based on single shot deep learning neural networks for object detection M. Kabolizadeh & M. Abbasi https://doi.org/10.61186/jgst.13.2.15
21. Multi-source Multimodal Data and Deep Learning for Disaster Response: A Systematic Review N. Algiriyage et al. https://doi.org/10.1007/s42979-021-00971-4
22. Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets C. Wu et al. https://doi.org/10.3390/rs13050905
23. BDD-Net: A General Protocol for Mapping Buildings Damaged by a Wide Range of Disasters Based on Satellite Imagery J. Shao et al. https://doi.org/10.3390/rs12101670
24. Detection of seismic façade damages with multi-temporal oblique aerial imagery D. Duarte et al. https://doi.org/10.1080/15481603.2020.1768768
25. Trialing Innovative Technologies in Crisis Management—“Airborne and Terrestrial Situational Awareness” as Support Tool in Flood Response E. Schröter et al. https://doi.org/10.3390/app10113743
26. Multi-Hazard and Spatial Transferability of a CNN for Automated Building Damage Assessment T. Valentijn et al. https://doi.org/10.3390/rs12172839
27. 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
28. A Novel Deep Learning Based Model for Tropical Intensity Estimation and Post-Disaster Management of Hurricanes J. Devaraj et al. https://doi.org/10.3390/app11094129
29. Lessons for Remote Post-earthquake Reconnaissance from the 14 August 2021 Haiti Earthquake M. Whitworth et al. https://doi.org/10.3389/fbuil.2022.873212
30. Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami Y. Bai et al. https://doi.org/10.3390/rs10101626
31. Classification of EgyptSat-1 Images Using Deep Learning Methods H. Keshk & X. Yin https://doi.org/10.2174/2210327909666190207153858
32. Leveraging Disruptive Technologies for Faster and More Efficient Disaster Response Management C. Calle Müller et al. https://doi.org/10.3390/su162310730
33. 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
34. Destruction from sky: Weakly supervised approach for destruction detection in satellite imagery M. Ali et al. https://doi.org/10.1016/j.isprsjprs.2020.02.002
35. 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
36. Automating tephra fall building damage assessment using deep learning E. Tennant et al. https://doi.org/10.5194/nhess-24-4585-2024
37. Detection of Collapsed Buildings in Post-Earthquake Remote Sensing Images Based on the Improved YOLOv3 H. Ma et al. https://doi.org/10.3390/rs12010044
38. Damage Detection and Segmentation in Disaster Environments Using Combined YOLO and Deeplab S. Jo et al. https://doi.org/10.3390/rs16224267
39. A novel multi-model fused classification for classifying the natural disaster G. Mondal & R. Dhanaraj https://doi.org/10.1007/s13748-025-00388-7
40. Urban 3D Structure Reconstruction Through a Generative Adversarial Network Model B. Aithal et al. https://doi.org/10.1007/s13369-020-04850-7
41. Rapid Assessment of Earthquake-Induced Building Damage Using Optical Image Correlation C. Li et al. https://doi.org/10.1109/TGRS.2026.3678102
42. Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks D. Duarte et al. https://doi.org/10.3390/rs10101636
43. Post-Disaster Building Damage Detection from Earth Observation Imagery Using Unsupervised and Transferable Anomaly Detecting Generative Adversarial Networks S. Tilon et al. https://doi.org/10.3390/rs12244193
44. UAV-Based Structural Damage Mapping: A Review N. Kerle et al. https://doi.org/10.3390/ijgi9010014
45. RETRACTED ARTICLE: Feature extraction and analysis of landscape imaging using drones and machine vision P. Li & J. Khan https://doi.org/10.1007/s00500-023-09352-w
46. Computer-Vision and Machine-Learning-Based Seismic Damage Assessment of Reinforced Concrete Structures Y. Xu et al. https://doi.org/10.3390/buildings13051258
47. Unsupervised learning framework for region-based damage assessment on xBD, a large satellite imagery P. Mittal et al. https://doi.org/10.1007/s11069-023-06074-y
48. Improved CNN Classification Method for Groups of Buildings Damaged by Earthquake, Based on High Resolution Remote Sensing Images H. Ma et al. https://doi.org/10.3390/rs12020260