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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

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

Papers

2019

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

2020

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

2021

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

2022

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

2023

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

Detection Papers

  1. CVNet: Contour Vibration Network for Building Extraction, 2022 | Paper
  2. 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

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