mlsanityData to 13 Oct 2026

Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks

arXiv 1910.06444 ↗First posted 14 Oct 2019cs.CV cs.LG eess.IVcited by 34

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Abstract (arXiv)

In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response. Remote sensing provides this data at an unprecedented scale, but extracting operationalizable information from satellite images is slow and labor-intensive. In this work, we use machine learning to automate the detection of building damage in satellite imagery. We compare the performance of four different convolutional neural network models in detecting damaged buildings in the 2010 Haiti earthquake. We also quantify how well the models will generalize to future disasters by training and testing models on different disaster events.

Key facts. Cited by 34 papers in the map (data through 13 Oct 2026). First posted on arXiv 14 Oct 2019.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 5; 2022: 9; 2023: 4; 2024: 9; 2025: 4; 2026: 3.

34 papers.

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  1. 2021Remote Sensing Image Change Detection with Transformerscited by 305
  2. 2024Mission Critical -- Satellite Data is a Distinct Modality in Machine Learningcited by 77
  3. 2024CDMamba: Incorporating Local Clues into Mamba for Remote Sensing Image Binary Change Detectioncited by 48
  4. 2022TINYCD: A (Not So) Deep Learning Model For Change Detectioncited by 36
  5. 2022Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidentscited by 26
  6. 2021BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Imagescited by 19
  7. 2022Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detectioncited by 16
  8. 2022SARAS-Net: Scale and Relation Aware Siamese Network for Change Detectioncited by 13
  9. 2023Continuous Cross-resolution Remote Sensing Image Change Detectioncited by 8
  10. 2025FoBa: A Foreground-Background co-Guided Method and New Benchmark for Remote Sensing Semantic Change Detectioncited by 6
  11. 2023Exploring Effective Priors and Efficient Models for Weakly-Supervised Change Detectioncited by 5
  12. 2024DeepDamageNet: A two-step deep-learning model for multi-disaster building damage segmentation and classification using satellite imagerycited by 5
22 more
  1. 2022BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledgecited by 4
  2. 2023End-to-end Remote Sensing Change Detection of Unregistered Bi-temporal Images for Natural Disasterscited by 4
  3. 2024Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Datacited by 4
  4. 2023Learning Efficient Unsupervised Satellite Image-based Building Damage Detectioncited by 3
  5. 2024An Open-Source Tool for Mapping War Destruction at Scale in Ukraine using Sentinel-1 Time Seriescited by 3
  6. 2021Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learningcited by 2
  7. 2021Building Damage Mapping with Self-PositiveUnlabeled Learningcited by 2
  8. 2021Disaster mapping from satellites: damage detection with crowdsourced point labelscited by 2
  9. 2022Towards Cross-Disaster Building Damage Assessment with Graph Convolutional Networkscited by 2
  10. 2024Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learningcited by 2
  11. 2024Cross Branch Feature Fusion Decoder for Consistency Regularization-based Semi-Supervised Change Detectioncited by 2
  12. 2022GaLeNet: Multimodal Learning for Disaster Prediction, Management and Reliefcited by 1
  13. 2022Multi-view deep learning for reliable post-disaster damage classificationcited by 1
  14. 2025Visual and Text Prompt Segmentation: A Novel Multi-Model Framework for Remote Sensingcited by 1
  15. 2025The Potential of Copernicus Satellites for Disaster Response: Retrieving Building Damage from Sentinel-1 and Sentinel-2cited by 1
  16. 2022Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagerycited by 0
  17. 2024Flood Data Analysis on SpaceNet 8 Using Apache Sedonacited by 0
  18. 2024GTPC-SSCD: Gate-guided Two-level Perturbation Consistency-based Semi-Supervised Change Detectioncited by 0
  19. 2025AI and Remote Sensing for Resilient and Sustainable Built Environments: A Review of Current Methods, Open Data and Future Directionscited by 0
  20. 2026Multi-Modal Building Inspection via Perceiver IO Fusion of Satellite and Street-Level Imagerycited by 0
  21. 2026DamageScope: Vision-Language Retrieval at Scale for Disaster Damage Assessment from Satellite Imagerycited by 0
  22. 2026Towards reliable multimodal disaster severity assessment through preference optimization and explainable vision-language reasoningcited by 0