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arXiv · 2009.01193

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

Maryam Rahnemoonfar·Tashnim Chowdhury·Robin Murphy·Odair Fernandes
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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.

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Maryam Rahnemoonfar, Tashnim Chowdhury, Robin Murphy, Odair Fernandes. 2020-09-02. Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment. https://arxiv.org/abs/2009.01193

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