Natural-Disaster-Damage-Assessment-Deep-Learning

Natural Disaster Damage Assessment

The datasets for damage assessments are divided into the following categories:

  1. Non-Imaging Data (Text, Tweets, Social Media Post)
  2. Imaging Dataset:
    1. Ground Level Images
    2. Aerial Imagery (UAV)
    3. Satellite Imagery

      Datasets

  3. xView, 2018 Satellite
  4. xView2, 2020 Satellite
  5. AIDER, 2020 UAV
  6. ISBDA, 2020 UAV
  7. Syria Destruction Dataset, 2021 Satellite
  8. LIVER-CD, 2021 Satellite
  9. FloodNet, 2021 UAV
  10. Ida-BD: Hurricane Ida, 2023 | Satellite

    Papers

    2019

  11. Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks, 2019 | Paper

    2020

  12. An Attention-Based System for Damage Assessment Using Satellite Imagery, 2020 Paper
  13. Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques, 2020 Paper
  14. BUILDING DISASTER DAMAGE ASSESSMENT IN SATELLITE IMAGERY WITH MULTI-TEMPORAL FUSION, 2020 Paper
  15. Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery, 2020 Paper
  16. Destruction from sky: weakly supervised approach for destruction detection in satllite imagery, 2020 Paper
  17. FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding, 2020 Paper
  18. RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery, 2020 | Paper

    2021

  19. Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets, 2021 Paper
  20. Weakly Supervised Segmentation of Small Buildings with Point Labels, 2021 | Paper

    2022

  21. Hybrid U-Net: Semantic segmentation of high-resolution satellite images to detect war destruction, 2022 Paper
  22. Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery, 2022 Paper
  23. Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets, 2022 Paper
  24. SegDetector: A Deep Learning Model for Detecting Small and Overlapping Damaged Buildings in Satellite Images, 2022 | Paper

    2023

  25. LARGE-SCALE BUILDING DAMAGE ASSESSMENT USING A NOVEL HIERARCHICAL TRANSFORMER ARCHITECTURE ON SATELLITE IMAGES, 2023 Paper
  26. xFBD: Focused Building Damage Dataset and Analysis, 2023 Paper
  27. RescueNet: A High Resolution UAV Semantic Segmentation Dataset for Natural Disaster Damage Assessment, 2023 | Paper | Code

    Detection Papers

  28. CVNet: Contour Vibration Network for Building Extraction, 2022 Paper
  29. PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images PP-LinkNet: Improving Semantic Segmentation of High Resolution Satellite Imagery with Multi-stage Training.pdf Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding.pdf

    Others

  30. SUSTAIN BENCH : Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning, 2021 Paper
  31. SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers, 2021 Paper Code