Earthquakes are deadly disasters that happen across the world, able to cause 750,000 deaths, comprising 55.6% of all natural hazard fatalities in a period of 20 years. To rescue survivors, damage assessment needs to be done quickly and accurately. While traditional on-site rescue and damage assessment is slow, satellite imaging provides insight into enabling quicker recovery.

My first goal is to compare the effectiveness of three common segmentation models (U-Net, DeepLabV3, and SegFormer) in earthquake damage assessment. My input data will be satellite images of damaged buildings after an earthquake with buildings at different damage levels, and I will use these to train these three models to compare their effectiveness, and understand their learning patterns—especially their weak points under limited training data. With my findings, I will then train and optimize one of the models to detect obstructed and unobstructed roads alongside the damaged buildings to prepare for the next step in expediting earthquake rescue, planning viable rescue routes. The goal of this project would be to have a working segmentation model that is able to assess earthquake damage of a site and identify unobstructed roads for potential rescue missions, while being lightweight for fast results.