Disaster mapping from satellites: damage detection with crowdsourced point labels
arXiv:2111.03693
Abstract
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.
3rd Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response at NeurIPS 2021
References in corpus (6)
- Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks
- Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion
- Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques
- Assessing out-of-domain generalization for robust building damage detection
- Multi-class segmentation under severe class imbalance: A case study in roof damage assessment
- Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery