Ioannou, E. orcid.org/0000-0003-3892-2492, Thalatam, S. and Georgescu, S. (2024) A synthetic dataset for semantic segmentation of waterbodies in out-of-distribution situations. Scientific Data, 11 (1). 1114. ISSN 2052-4463
Abstract
In the past decade, substantial global efforts have been devoted to the development of reliable and efficient solutions for early flood warning and monitoring. One of the most common strategies for tackling this challenge involves the application of computer vision techniques to images obtained from the numerous surveillance cameras present in urban settings today. While there are various datasets available for training and testing these techniques, none of them specifically addresses the issue of out-of-distribution (OoD) behavior. This issue becomes particularly critical when evaluating the reliability of these methods under challenging environmental conditions. Our work stands as the first attempt to bridge this gap by introducing a new, highly controlled synthetic dataset that encompasses the essential attributes required for analyzing OoD behavior. The very high correlation between the accuracy of artificial intelligence (AI) models trained on our synthetic dataset and models trained on real-world data proves our dataset’s ability to predict real-world OoD behavior reliably.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | Environmental impact; Water resources |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Oct 2024 08:12 |
Last Modified: | 16 Oct 2024 08:12 |
Status: | Published |
Publisher: | Springer Science and Business Media LLC |
Refereed: | Yes |
Identification Number: | 10.1038/s41597-024-03929-2 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:218393 |