  The detrimental impacts of climate change include stronger and more
destructive hurricanes happening all over the world. Identifying different
damaged structures of an area including buildings and roads are vital since it
helps the rescue team to plan their efforts to minimize the damage caused by a
natural disaster. Semantic segmentation helps to identify different parts of an
image. We implement a novel self-attention based semantic segmentation model on
a high resolution UAV dataset and attain Mean IoU score of around 88% on the
test set. The result inspires to use self-attention schemes in natural disaster
damage assessment which will save human lives and reduce economic losses.
