mlsanityData to 7 Oct 2026

BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response

arXiv 2501.06019 ↗First posted 10 Jan 2025cs.CV cs.AI eess.IVcited by 39

Not on the map. Why?

Abstract (arXiv)

Disaster events occur around the world and cause significant damage to human life and property. Earth observation (EO) data enables rapid and comprehensive building damage assessment (BDA), an essential capability in the aftermath of a disaster to reduce human casualties and to inform disaster relief efforts. Recent research focuses on the development of AI models to achieve accurate mapping of unseen disaster events, mostly using optical EO data. However, solutions based on optical data are limited to clear skies and daylight hours, preventing a prompt response to disasters. Integrating multimodal (MM) EO data, particularly the combination of optical and SAR imagery, makes it possible to provide all-weather, day-and-night disaster responses. Despite this potential, the development of robust multimodal AI models has been constrained by the lack of suitable benchmark datasets. In this paper, we present a BDA dataset using veRy-hIGH-resoluTion optical and SAR imagery (BRIGHT) to support AI-based all-weather disaster response. To the best of our knowledge, BRIGHT is the first open-access, globally distributed, event-diverse MM dataset specifically curated to support AI-based disaster response. It covers five types of natural disasters and two types of man-made disasters across 14 regions worldwide, with a particular focus on developing countries where external assistance is most needed. The optical and SAR imagery in BRIGHT, with a spatial resolution between 0.3-1 meters, provides detailed representations of individual buildings, making it ideal for precise BDA. In our experiments, we have tested seven advanced AI models trained with our BRIGHT to validate the transferability and robustness. The dataset and code are available at https://github.com/ChenHongruixuan/BRIGHT. BRIGHT also serves as the official dataset for the 2025 IEEE GRSS Data Fusion Contest.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). Builds on 36 of its 143 references that are in the map. First posted on arXiv 10 Jan 2025.

Builds on

36 of 143 references.

References · 36

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  4. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  5. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  6. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  7. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  8. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  9. 2018ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentationcited by 623
  10. 2018Learning to Adapt Structured Output Space for Semantic Segmentationcited by 584
  11. 2020FDA: Fourier Domain Adaptation for Semantic Segmentationcited by 540
  12. 2019Generalizing from a Few Examples: A Survey on Few-Shot Learningcited by 462
24 more
  1. 2021Semi-Supervised Semantic Segmentation with Cross Pseudo Supervisioncited by 462
  2. 2020Semi-Supervised Semantic Segmentation with Cross-Consistency Trainingcited by 375
  3. 2017One-Shot Learning for Semantic Segmentationcited by 337
  4. 2018Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptationcited by 295
  5. 2023SpectralGPT: Spectral Remote Sensing Foundation Modelcited by 250
  6. 2020FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understandingcited by 181
  7. 2022Advancing Plain Vision Transformer Towards Remote Sensing Foundation Modelcited by 149
  8. 2020Guided Collaborative Training for Pixel-wise Semi-Supervised Learningcited by 120
  9. 2019Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approachcited by 103
  10. 2021Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Datacited by 41
  11. 2022RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessmentcited by 40
  12. 2022Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detectioncited by 26
  13. 2020RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagerycited by 24
  14. 2022Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Imagescited by 24
  15. 2020Learning from Multimodal and Multitemporal Earth Observation Data for Building Damage Mappingcited by 21
  16. 2021BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Imagescited by 19
  17. 2022Dual-Tasks Siamese Transformer Framework for Building Damage Assessmentcited by 19
  18. 2023Can We Evaluate Domain Adaptation Models Without Target-Domain Labels?cited by 14
  19. 2023SAAN: Similarity-aware attention flow network for change detection with VHR remote sensing imagescited by 12
  20. 2024CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagerycited by 9
  21. 2023QuickQuakeBuildings: Post-earthquake SAR-Optical Dataset for Quick Damaged-building Detectioncited by 6
  22. 2024A simple, strong baseline for building damage detection on the xBD datasetcited by 4
  23. 2024Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approachcited by 4
  24. 2024An Open-Source Tool for Mapping War Destruction at Scale in Ukraine using Sentinel-1 Time Seriescited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 15; 2026: 24.

39 papers.

Sort:

By area:

  1. 2025DisasterM3: A Remote Sensing Vision-Language Dataset for Disaster Damage Assessment and Responsecited by 35
  2. 2025SARLANG-1M: A Benchmark for Vision-Language Modeling in SAR Image Understandingcited by 16
  3. 2025OpenEarthMap-SAR: A Benchmark Synthetic Aperture Radar Dataset for Global High-Resolution Land Cover Mappingcited by 13
  4. 2025SARMAE: Masked Autoencoder for SAR Representation Learningcited by 6
  5. 2025Enhancing Monocular Height Estimation via Sparse LiDAR-Guided Correctioncited by 4
  6. 2026Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operationscited by 4
  7. 2025SAR-TEXT: A Large-Scale SAR Image-Text Dataset Built with SAR-Narrator and A Progressive Learning Strategy for Downstream Taskscited by 3
  8. 2026OmniEarth: A Benchmark for Evaluating Vision-Language Models in Geospatial Taskscited by 3
  9. 2026MM-OVSeg:Multimodal Optical-SAR Fusion for Open-Vocabulary Segmentation in Remote Sensingcited by 3
  10. 2025Now you see it, Now you don't: Damage Label Agreement in Drone & Satellite Post-Disaster Imagerycited by 2
  11. 2026DisasterInsight: A Building-Centric Benchmark for Evaluating Vision--Language Models in Disaster Responsecited by 2
  12. 2025Building-Guided Pseudo-Label Learning for Cross-Modal Building Damage Mappingcited by 1
27 more
  1. 2025Semantic Change Detection of Roads and Bridges: A Fine-grained Dataset and Multimodal Frequency-driven Detectorcited by 1
  2. 2025Text-conditioned State Space Model For Domain-generalized Change Detection Visual Question Answeringcited by 1
  3. 2025The Potential of Copernicus Satellites for Disaster Response: Retrieving Building Damage from Sentinel-1 and Sentinel-2cited by 1
  4. 2025ChessMamba: Structure-Aware Interleaving of State Spaces for Change Detection in Remote Sensing Imagescited by 1
  5. 2026Smart Transfer: Leveraging Vision Foundation Model for Rapid Building Damage Mapping with Post-Earthquake VHR Imagerycited by 1
  6. 2025Exploring Generalizable Pre-training for Real-world Change Detection via Geometric Estimationcited by 0
  7. 2025A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquakecited by 0
  8. 2025AI and Remote Sensing for Resilient and Sustainable Built Environments: A Review of Current Methods, Open Data and Future Directionscited by 0
  9. 2026Matching of SAR and optical images based on transformation to shared modalitycited by 0
  10. 2026Multi-Modal Building Change Detection for Large-Scale Small Changes: Benchmark and Baselinecited by 0
  11. 2026Prior-guided Fusion of Multimodal Features for Change Detection from Optical-SAR Imagescited by 0
  12. 2026A Mamba-Based Multimodal Network for Multiscale Blast-Induced Rapid Structural Damage Assessmentcited by 0
  13. 2026Better with Less: Tackling Heterogeneous Multi-Modal Image Joint Pretraining via Conditioned and Degraded Masked Autoencodercited by 0
  14. 2026ChangeQuery: Advancing Remote Sensing Change Analysis for Natural and Human-Induced Disasters from Visual Detection to Semantic Understandingcited by 0
  15. 2026Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applicationscited by 0
  16. 2026FAF-CD: Frequency-Aware Fusion for Change Detection under Imperfect Multimodal Remote Sensingcited by 0
  17. 2026RAPID: A Reproducible Multi-Agent Pipeline for Interpretable Disaster Damage Assessment from Satellite and Street-View Imagerycited by 0
  18. 2026JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answeringcited by 0
  19. 2026HASTE: A Platform for Rapid Post-Disaster Building Damage Assessmentcited by 0
  20. 2026Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challengecited by 0
  21. 2026Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streamscited by 0
  22. 2026GeoCore-9B: Towards Geo-Aware Generative Foundation Models in Earth Observationcited by 0
  23. 2026GeoSeg-OV: Bridging Geospatial Gaps with Structural Guidance for Open-Vocabulary Remote Sensing Segmentationcited by 0
  24. 2026Keep Your Friends Close, and the Right Neighbours Closer: Disaster-Conditioned Kernel-Regularized Graph Attention for Building Damage Classificationcited by 0
  25. 2026GeBDA: Building Damage Assessment as Text-Based Sequence Predictioncited by 0
  26. 2026RAPIDMap: Rapid Multi-Agent Pipeline for Interpretable Disaster Mapping from Satellite and Street-view Imagerycited by 0
  27. 2026Multimodal Foundation Models Adaptation based on Domain-Aware Relaxed Orthogonal Subspace for Remote Sensingcited by 0