Disaster mapping from satellites: damage detection with crowdsourced point labels
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Abstract (arXiv)
High-resolution satellite imagery available immediately after disaster events is crucial for response planning as it facilitates broad situational awareness of critical infrastructure status such as building damage, flooding, and obstructions to access routes. Damage mapping at this scale would require hundreds of expert person-hours. However, a combination of crowdsourcing and recent advances in deep learning reduces the effort needed to just a few hours in real time. Asking volunteers to place point marks, as opposed to shapes of actual damaged areas, significantly decreases the required analysis time for response during the disaster. However, different volunteers may be inconsistent in their marking. This work presents methods for aggregating potentially inconsistent damage marks to train a neural network damage detector.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 17 of its 24 references that are in the map. First posted on arXiv 5 Nov 2021.
Builds on
17 of 24 references.
References · 17
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2015What's the Point: Semantic Segmentation with Point Supervisioncited by 298
- 2018DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Imagescited by 277
- 2019Bottom-up Object Detection by Grouping Extreme and Center Pointscited by 156
- 2018xView: Objects in Context in Overhead Imagerycited by 146
- 2019xBD: A Dataset for Assessing Building Damage from Satellite Imagerycited by 139
- 2017Deep Extreme Cut: From Extreme Points to Object Segmentationcited by 127
- 2019Large-scale interactive object segmentation with human annotatorscited by 110
- 2019Building Damage Detection in Satellite Imagery Using Convolutional Neural Networkscited by 34
- 2020Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusioncited by 27
- 2020RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagerycited by 24
5 more
- 2020Assessing out-of-domain generalization for robust building damage detectioncited by 13
- 2020Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniquescited by 10
- 2020Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagerycited by 4
- 2018BCCNet: Bayesian classifier combination neural networkcited by 2
- 2020Multi-class segmentation under severe class imbalance: A case study in roof damage assessmentcited by 2
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Citing papers per year: 2022: 2.
2 papers.