Attention Based Semantic Segmentation on UAV Dataset for Natural Disaster Damage Assessment

TL;DR

This paper presents a self-attention based semantic segmentation model applied to UAV imagery for natural disaster damage assessment, achieving high accuracy and aiding rescue efforts.

Contribution

The paper introduces a novel self-attention based semantic segmentation approach tailored for high-resolution UAV datasets in disaster scenarios.

Findings

Achieved around 88% Mean IoU on test set

Demonstrated effectiveness of self-attention in damage assessment

Potential to improve rescue planning and damage evaluation

Abstract

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.