Satellite imagery analysis for operational damage assessment in Emergency situations
This addresses the need for faster damage assessment to aid decision-making by local authorities and humanitarian teams in emergency situations.
The paper tackles the problem of timely damage assessment after disasters by using machine learning and computer vision on satellite imagery, demonstrating its workflow on 2017 California wildfires to improve time efficiency.
When major disaster occurs the questions are raised how to estimate the damage in time to support the decision making process and relief efforts by local authorities or humanitarian teams. In this paper we consider the use of Machine Learning and Computer Vision on remote sensing imagery to improve time efficiency of assessment of damaged buildings in disaster affected area. We propose a general workflow that can be useful in various disaster management applications, and demonstrate the use of the proposed workflow for the assessment of the damage caused by the wildfires in California in 2017.