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When major disaster occurs the questions are raised how to estimate thedamage in time to support the decision making process and relief efforts bylocal authorities or humanitarian teams. In this paper we consider the use ofMachine Learning and Computer Vision on remote sensing imagery to improve timeefficiency of assessment of damaged buildings in disaster affected area. Wepropose a general workflow that can be useful in various disaster managementapplications, and demonstrate the use of the proposed workflow for theassessment of the damage caused by the wildfires in California in 2017.

Satellite imagery analysis for operational damage assessment in Emergency situations