Attention Based Semantic Segmentation on UAV Dataset for Natural Disaster Damage Assessment
This work addresses the need for efficient damage assessment to aid rescue teams in minimizing human and economic losses after natural disasters.
The paper tackled the problem of identifying damaged structures like buildings and roads from UAV imagery for natural disaster damage assessment, achieving a Mean IoU score of around 88% on a test set.
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.