# Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment

Source: [Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment](<https://www.sota2.com/research/paper/68fa68461e209131cea1af9a>)

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

Data updated: 2025-10-25T22:33:00.328Z

Authors: Maryam Rahnemoonfar, Tashnim Chowdhury, Robin Murphy, Odair Fernandes

Published: 2020-09-02T17:07:28.000Z

## Abstract

In this paper, we present a large-scale hurricane Michael dataset for visual perception in disaster scenarios, and analyze state-of-the-art deep neural network models for semantic segmentation. The dataset consists of around 2000 high-resolution aerial images, with annotated ground-truth data for semantic segmentation. We discuss the challenges of the dataset and train the state-of-the-art methods on this dataset to evaluate how well these methods can recognize the disaster situations. Finally, we discuss challenges for future research.

## 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/2009.01193>)

[PDF](<https://arxiv.org/pdf/2009.01193.pdf>)