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

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 se...

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Veröffentlicht in:	arXiv.org 2020-09
Hauptverfasser:	Rahnemoonfar, Maryam, Chowdhury, Tashnim, Murphy, Robin, Fernandes, Odair
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Sprache:	eng
Schlagworte:	Artificial neural networks Datasets High resolution Image resolution Image segmentation Natural disasters Semantic segmentation Semantics Visual perception
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creator	Rahnemoonfar, Maryam Chowdhury, Tashnim Murphy, Robin Fernandes, Odair
description	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.
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subjects	Artificial neural networks Datasets High resolution Image resolution Image segmentation Natural disasters Semantic segmentation Semantics Visual perception
title	Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment
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