Chowdhury, TashnimRahnemoonfar, Maryam2021-06-152021-06-152021-06-01Chowdhury, Tashnim; Rahnemoonfar, Maryam; Attention Based Semantic Segmentation on UAV Dataset for Natural Disaster Damage Assessment; Computer Vision and Pattern Recognition, 1 June, 2021; https://arxiv.org/abs/2105.14540http://hdl.handle.net/11603/21746The 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.4 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Attribution 4.0 International (CC BY 4.0)UMBC Computer Vision and Remote Sensing Laboratory (Bina Lab)Text