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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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Averaged statistics of the different models over the WF-6 dataset. This benchmark was run on a SpaceCloud device with an Intel X5 processor and a Intel Movidius Myriad2 chip. (a) Compression ratio of the final vectorised products (input size divided by output size) produced by the different ML models and baselines. (b–d) Intersection over Union, precision and recall of those models measured against the ground truth of the WorldFloods dataset¹². (e) Total time (inference and vectorization) of the models running on different hardware. (f) Size of the weights in KB; this size is critical for updating the model after the satellite deployment.