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

Source: [Attention Based Semantic Segmentation on UAV Dataset for Natural Disaster Damage Assessment](<https://www.sota2.com/research/paper/68fa6a101e209131cea25ee4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:48.167Z

Authors: Tashnim Chowdhury, Maryam Rahnemoonfar

Published: 2021-05-30T13:39:03.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2105.14540>)

[PDF](<https://arxiv.org/pdf/2105.14540.pdf>)