Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment
| dc.contributor.author | Rahnemoonfar, Maryam | |
| dc.contributor.author | Chowdhury, Tashnim | |
| dc.contributor.author | Murphy, Robin | |
| dc.contributor.author | Fernandes, Odair | |
| dc.date.accessioned | 2020-11-02T19:46:41Z | |
| dc.date.available | 2020-11-02T19:46:41Z | |
| dc.date.issued | 2020-09-02 | |
| dc.description.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. | en_US |
| dc.description.sponsorship | This work is supported in part by Microsoft. Annotations are performed on the V7 Darwin platform. | en_US |
| dc.description.uri | https://arxiv.org/abs/2009.01193 | en_US |
| dc.format.extent | 10 pages | en_US |
| dc.genre | journal articles preprints | en_US |
| dc.identifier | doi:10.13016/m2aiqb-dezo | |
| dc.identifier.citation | 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 | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/19995 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.subject | UMBC Computer Vision and Remote Sensing Laboratory | |
| dc.title | Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment | en_US |
| dc.type | Text | en_US |