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