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. 2023 Jun 27;13:10391. doi: 10.1038/s41598-023-34436-w

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© The Author(s) 2023

Open AccessThis 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/.

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Intersection over Union (IoU) on the D-Sense labeled dataset (D-Sense-4) of the different models depending on the dataset used for training. The performance of the models trained in the WorldFloods dataset is very poor due to the differences between S2 and D-Sense images. When we train on the D-Sense-4 data using a leave-one-out training scheme we obtain satisfactory segmentations. Training the models from scratch or fine-tuning the WorldFloods models produce similar results on D-Sense labeled data.