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

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2009.01193.pdf (2.32 MB)

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https://arxiv.org/abs/2009.01193

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http://hdl.handle.net/11603/19995

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UMBC Information Systems Department
UMBC Faculty Collection

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Date

2020-09-02

Type of Work

journal articles preprints
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Citation of Original Publication

Maryam Rahnemoonfar, Tashnim Chowdhury, Robin Murphy and Odair Fernandes, Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment, https://arxiv.org/abs/2009.01193

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Subjects

UMBC Computer Vision and Remote Sensing Laboratory

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.