Disaster mapping from satellites:damage detection with crowdsourced point labels

Kuzin, Danil and Simmons, Brooke and Isupova, Olga and Reece, Steven (2021) Disaster mapping from satellites:damage detection with crowdsourced point labels. In: NeurIPS 2021, 2021-12-062021-12-14, Virtual.

[img]
Text (Disaster mapping with crowdsourcing arXiv version)
2111.03693.pdf - Accepted Version

Download (4MB)

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.

Item Type:
Contribution to Conference (Paper)
Journal or Publication Title:
NeurIPS 2021
Additional Information:
3rd Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response (NeurIPS 2021)
ID Code:
167374
Deposited By:
Deposited On:
25 Oct 2022 13:23
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Nov 2022 14:52