44 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.
2. Unsupervised learning framework for region-based damage assessment on xBD, a large satellite imagery P. Mittal et al.
3. A Fast and Lightweight Method for Detecting Damaged Buildings After Earthquakes D. Yang & Z. Feng
4. Post-earthquake recovery in coastal cities of Manabí, Ecuador: A regional assessment nine years after the 2016 Muisne earthquake B. Cagua & R. Aguiar
5. A Synthetic Aperture Radar Imaging Simulation Method for Sea Surface Scenes Combined with Electromagnetic Scattering Characteristics Y. He et al.
6. A Novel UNet 3+ Change Detection Method Considering Scale Uncertainty in High-Resolution Imagery T. Bai et al.
7. Integrating InSAR and Channel Steepness for AI-Based Coseismic Landslide Modeling in the Nepal Himalaya R. Silwal et al.
8. Deep Ensemble Learning for Rapid Large-Scale Postearthquake Damage Assessment: Application to Satellite Images from the 2023 Türkiye Earthquakes M. Soleimani-Babakamali et al.
9. Обґрунтування переваг у використанні оптичних та радарних даних дзз при виявленні будівель, порушених внаслідок природного чи антропогенного впливу Л. Скрипник et al.
10. Scalable variational learning for noisy-OR Bayesian networks with normalizing flows for complex cascading disaster systems X. Li & S. Xu
11. Multimodal Building Damage Assessment Method Fusing Adaptive Attention Mechanism and State-Space Modeling R. Zhu & X. Lan
12. Multiclass post-earthquake building assessment integrating high-resolution optical and SAR satellite imagery, ground motion, and soil data with transformers D. Singh et al.
13. DDFormer: A Dual-Domain Transformer for Building Damage Detection Using High-Resolution SAR Imagery T. Li et al.
14. Data collection tools for post-disaster damage assessment of building and lifeline infrastructure systems J. Lozano & I. Tien
15. Toward Reliable Post-Disaster Assessment: Advancing Building Damage Detection Using You Only Look Once Convolutional Neural Network and Satellite Imagery C. Moreno González et al.
16. Stepwise Multisensor Estimation of Shelter Hazard and Lifeline Outages for Disaster Response and Resilience: A Case Study of the 2024 Noto Peninsula Earthquake S. Kimijima et al.
17. Assessing the transferability of post-disaster building damage assessment using synthetic aperture radar and machine learning J. Currie & K. Reinke
18. Comparative analysis of deep feature fusion and machine learning classifiers for UAV imagery in post-earthquake building damage assessment U. Şevik & A. Yilmaz
19. Evaluation of Ship Detection Capability in SAR Images: A Study Based on Convolutional Neural Networks and Objective Assessment Metrics P. Zhou et al.
20. Deep learning-based automated tile defect detection system for Portuguese cultural heritage buildings N. Karimi et al.
21. Unsupervised SAR Image Change Detection Based on Curvelet Fusion and Local Patch Similarity Information Clustering Y. Huang et al.
22. InCR: Inception and concatenation residual block-based deep learning network for damaged building detection using remote sensing images B. Tasci et al.
23. A machine learning-supported rapid classification of building damage following the 2010–2011 Canterbury earthquakes L. Li et al.
24. Advances in Rapid Damage Identification Methods for Post-Disaster Regional Buildings Based on Remote Sensing Images: A Survey J. Gu et al.
25. Building damage detection from multi-feature fusion of Sentinel-1/2 imagery using variational autoencoder and MLP-Mixer network: insights from the Jishishan earthquake J. Wang et al.
26. Earthquake building damage classification based on full suite of Sentinel-1 features X. Lv et al.
27. Assessment of building damage from the 2020 Sivrice earthquake using a satellite based rapid seismic screening method Y. Gedik et al.
28. Advancing Rural Building Extraction via Diverse Dataset Construction and Model Innovation with Attention and Context Learning M. Yu et al.
29. ASAI: A general and training-free artificial surfaces anomaly index using post-disaster single-temporal and high-resolution imagery S. Ren et al.
30. Evaluating fine tuned deep learning models for real-time earthquake damage assessment with drone-based images F. Kizilay et al.
31. Flood change detection model based on an improved U-net network and multi-head attention mechanism F. Wang & X. Feng
32. A Stacking Ensemble Approach for Postdisaster Building Damage Assessment S. Lin et al.
33. Estimating Road Disruptions in Urban Contexts Due to Earthquakes Using Machine Learning Surrogates C. Costa & V. Silva
34. Improving the Generalization Performance of Multi-Earthquake-Case Models for Building Damage Assessments Based on Multi-Sensor Data and Model Weight Optimization J. Chen et al.
35. Artificial intelligence-based assessment of large-scale building safety risk in non-disaster scenarios by integrating InSAR-derived subsidence and environmental remote sensing data J. Pan et al.
36. Causal spatially heterogeneous Bayesian networks with GPs and normalizing flows for seismic multi-hazard estimation X. Li et al.
37. Yapay Zeka Kullanılarak Deprem Bölgelerinden Drone ile Alınan Görüntülerden Enkaz Tespitinin Gerçekleştirilmesi Ö. Picak & K. Sabancı
38. Natural disaster damage analysis using lightweight spatial feature aggregated deep learning model K. Abraham et al.
39. Assessing the Impact of the 2023 Kahramanmaras Earthquake on Cultural Heritage Sites Using High-Resolution SAR Images C. Boyoğlu et al.
40. A Big Data-Enabled Decision Support Model for Post-Earthquake Damage Classification of RC Buildings: A Case Study on February 6, Kahramanmaraş Doublet Earthquakes S. Mostofi et al.
41. Vibration-based building health monitoring using spatio-temporal learning model V. Dang & H. Pham
42. Multi-task building damage assessment via deep semantic segmentation and pre-disaster polygons S. Alpergin et al.
43. Long-term tracking of recovery of built infrastructure after wildfires with deep network topologies A. Schmidt et al.
44. The AlexNet HSD model for industrial heritage damage detection and adaptive reuse under artificial intelligence H. Zhang