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

• URL: http://arxiv.org/abs/2009.01193v2
• Date: Sun, 6 Sep 2020 22:03:20 GMT
• Title: Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment
• Authors: Maryam Rahnemoonfar, Tashnim Chowdhury, Robin Murphy, Odair Fernandes
• Abstract summary: We present a large-scale hurricane Michael dataset for visual perception in disaster scenarios. We analyze state-of-the-art deep neural network models for semantic segmentation.
• Score: 0.26249027950824505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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