└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Natural Disaster Damage Assessment 2 | The datasets for damage assessments are divided into the following categories: 3 | 1. Non-Imaging Data (Text, Tweets, Social Media Post) 4 | 2. Imaging Dataset: 5 | 1. Ground Level Images 6 | 2. Aerial Imagery (UAV) 7 | 3. Satellite Imagery 8 | ## Datasets 9 | 1. [xView](http://xviewdataset.org/), 2018 | Satellite 10 | 2. [xView2](https://xview2.org/), 2020 | Satellite 11 | 3. [AIDER](https://github.com/ckyrkou/AIDER), 2020 | UAV 12 | 4. [ISBDA](https://drive.google.com/file/d/1kEKJ8kr1aScXz_1El7Mn-Yi0ANducQIW/view), 2020 | UAV 13 | 5. [Syria Destruction Dataset](https://github.com/ShimaN19/Hybrid-U-Net/tree/main), 2021 | Satellite 14 | 6. [LIVER-CD](https://chenhao.in/LEVIR/), 2021 | Satellite 15 | 7. [FloodNet](https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021), 2021 | UAV 16 | 8. [Ida-BD: Hurricane Ida](https://www.designsafe-ci.org/data/browser/public/designsafe.storage.published//PRJ-3563/images), 2023 | Satellite 17 | ## Papers 18 | #### 2019 19 | 1. Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks, 2019 | [Paper](https://arxiv.org/abs/1910.06444) 20 | #### 2020 21 | 1. An Attention-Based System for Damage Assessment Using Satellite Imagery, 2020 | [Paper](https://arxiv.org/abs/2004.06643) 22 | 2. Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques, 2020 | [Paper](https://arxiv.org/abs/2011.14004) 23 | 3. BUILDING DISASTER DAMAGE ASSESSMENT IN SATELLITE IMAGERY WITH MULTI-TEMPORAL FUSION, 2020 | [Paper](https://arxiv.org/abs/2004.05525) 24 | 4. Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery, 2020 | [Paper](https://arxiv.org/abs/2010.14014) 25 | 5. Destruction from sky: weakly supervised approach for destruction detection in satllite imagery, 2020 | [Paper](https://im.itu.edu.pk/destruction-detection/) 26 | 6. FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding, 2020 | [Paper](https://arxiv.org/abs/2012.02951) 27 | 7. RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery, 2020 | [Paper](https://arxiv.org/abs/2004.07312) 28 | #### 2021 29 | 1. Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets, 2021 | [Paper](https://www.mdpi.com/2072-4292/13/5/905) 30 | 2. Weakly Supervised Segmentation of Small Buildings with Point Labels, 2021 | [Paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Lee_Weakly_Supervised_Segmentation_of_Small_Buildings_With_Point_Labels_ICCV_2021_paper.pdf) 31 | #### 2022 32 | 1. Hybrid U-Net: Semantic segmentation of high-resolution satellite images to detect war destruction, 2022 | [Paper](https://www.sciencedirect.com/science/article/pii/S2666827022000688) 33 | 2. Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery, 2022 | [Paper](https://arxiv.org/abs/2201.10523) 34 | 3. Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets, 2022 | [Paper](https://arxiv.org/abs/2205.15688) 35 | 4. SegDetector: A Deep Learning Model for Detecting Small and Overlapping Damaged Buildings in Satellite Images, 2022 | [Paper](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwi944nV4rP_AhW4QvEDHZqIASEQFnoECCAQAQ&url=https%3A%2F%2Fwww.mdpi.com%2F2072-4292%2F14%2F23%2F6136%2Fpdf&usg=AOvVaw3a4D8Kf08nBCgeg7G8jwHx) 36 | #### 2023 37 | 1. LARGE-SCALE BUILDING DAMAGE ASSESSMENT USING A NOVEL HIERARCHICAL TRANSFORMER ARCHITECTURE ON SATELLITE IMAGES, 2023 | [Paper](https://arxiv.org/pdf/2208.02205.pdf) 38 | 2. xFBD: Focused Building Damage Dataset and Analysis, 2023 | [Paper](https://arxiv.org/pdf/2212.13876.pdf) 39 | 3. RescueNet: A High Resolution UAV Semantic Segmentation Dataset for Natural Disaster Damage Assessment, 2023 | [Paper](https://arxiv.org/pdf/2202.12361.pdf) | [Code](https://github.com/BinaLab/RescueNet-A-High-Resolution-Post-Disaster-UAV-Dataset-for-Semantic-Segmentation) 40 | ## Detection Papers 41 | 1. CVNet: Contour Vibration Network for Building Extraction, 2022 | [Paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_CVNet_Contour_Vibration_Network_for_Building_Extraction_CVPR_2022_paper.pdf) 42 | 2. PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images 43 | PP-LinkNet: Improving Semantic Segmentation of High Resolution Satellite Imagery with Multi-stage Training.pdf 44 | Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding.pdf 45 | #### Others 46 | 1. SUSTAIN BENCH : Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning, 2021 | [Paper](https://arxiv.org/abs/2111.04724) 47 | 2. SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers, 2021 | [Paper](https://arxiv.org/pdf/2105.15203.pdf) | [Code](https://github.com/nka77/dahitra) 48 | --------------------------------------------------------------------------------