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Communication Dans Un Congrès Année : 2023

Analysis of sea surface temperature maps via topological machine learning

Résumé

Computational methods to leverage topological features occurring in signals and images are currently one of the most innovative trends in applied mathematics. In this paper a pipeline of topological machine learning is applied to the challenging task of classifying four specific marine mesoscale patterns from remote sensing data, i.e., Sea Surface Temperature maps of the southwestern region of the Iberian Peninsula. Our preliminary study achieves an accuracy of 56% in the 4-label classification. Such results are encouraging, especially considering that the data are affected by noise and that there are low-quality/missing data. Also, the paper devises directions for future improvements.
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Dates et versions

hal-04125467 , version 1 (12-06-2023)

Identifiants

Citer

Francesco Conti, Oscar Papini, Davide Moroni, Gabriele Pieri, Marco Reggiannini, et al.. Analysis of sea surface temperature maps via topological machine learning. 2023 IX International Conference on Information Technology and Nanotechnology (ITNT), Apr 2023, Samara, Russia. pp.1-4, ⟨10.1109/itnt57377.2023.10139044⟩. ⟨hal-04125467⟩
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