mlsanityData to 7 Oct 2026

Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion

arXiv 2004.05525 ↗First posted 12 Apr 2020cs.CVcited by 27

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Abstract (arXiv)

Automatic change detection and disaster damage assessment are currently procedures requiring a huge amount of labor and manual work by satellite imagery analysts. In the occurrences of natural disasters, timely change detection can save lives. In this work, we report findings on problem framing, data processing and training procedures which are specifically helpful for the task of building damage assessment using the newly released xBD dataset. Our insights lead to substantial improvement over the xBD baseline models, and we score among top results on the xView2 challenge leaderboard. We release our code used for the competition.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 7; 2023: 3; 2024: 4; 2025: 7; 2026: 3.

27 papers.

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  1. 2024ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Modelcited by 118
  2. 2022Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidentscited by 25
  3. 2022Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Imagescited by 24
  4. 2021BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Imagescited by 19
  5. 2022Dual-Tasks Siamese Transformer Framework for Building Damage Assessmentcited by 19
  6. 2023Unleashing the Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenariocited by 10
  7. 2024Generalizable Disaster Damage Assessment via Change Detection with Vision Foundation Modelcited by 10
  8. 2025DamageCAT: A Deep Learning Transformer Framework for Typology-Based Post-Disaster Building Damage Categorizationcited by 8
  9. 2025TaCo: Capturing Spatio-Temporal Semantic Consistency in Remote Sensing Change Detectioncited by 6
  10. 2022BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledgecited by 4
  11. 2022Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasetscited by 4
  12. 2024Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Datacited by 4
15 more
  1. 2025IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorizationcited by 4
  2. 2023Learning Efficient Unsupervised Satellite Image-based Building Damage Detectioncited by 3
  3. 2021Disaster mapping from satellites: damage detection with crowdsourced point labelscited by 2
  4. 2022GaLeNet: Multimodal Learning for Disaster Prediction, Management and Reliefcited by 1
  5. 2024Multi-step feature fusion for natural disaster damage assessment on satellite imagescited by 1
  6. 2025The Potential of Copernicus Satellites for Disaster Response: Retrieving Building Damage from Sentinel-1 and Sentinel-2cited by 1
  7. 2025Think First, Assign Next (ThiFAN-VQA): A Two-stage Chain-of-Thought Framework for Post-Disaster Damage Assessmentcited by 1
  8. 2021Runway Extraction and Improved Mapping from Space Imagerycited by 0
  9. 2022Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagerycited by 0
  10. 2023AB2CD: AI for Building Climate Damage Classification and Detectioncited by 0
  11. 2025AI and Remote Sensing for Resilient and Sustainable Built Environments: A Review of Current Methods, Open Data and Future Directionscited by 0
  12. 2025UniRSCD: A Unified Novel Architectural Paradigm for Remote Sensing Change Detectioncited by 0
  13. 2026ChangeQuery: Advancing Remote Sensing Change Analysis for Natural and Human-Induced Disasters from Visual Detection to Semantic Understandingcited by 0
  14. 2026HASTE: A Platform for Rapid Post-Disaster Building Damage Assessmentcited by 0
  15. 2026Keep Your Friends Close, and the Right Neighbours Closer: Disaster-Conditioned Kernel-Regularized Graph Attention for Building Damage Classificationcited by 0