Document - Analysis of sea surface temperature maps via topological machine learning

2023

Conference article Open Access

Analysis of sea surface temperature maps via topological machine learning

Conti F., Papini O., Moroni D., Pieri G., Reggiannini M., Pascali M. A.

TDA Sea surface temperature map Marine mesoscale patterns Remote sensing

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.

Source: ITNT 2023 - IX International Conference on Information Technology and Nanotechnology, Samara, Russia, 17-21/04/2023

Publisher: IEEE, New York, USA

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BibTeX entry

@inproceedings{oai:it.cnr:prodotti:482371,
	title = {Analysis of sea surface temperature maps via topological machine learning},
	author = {Conti F. and Papini O. and Moroni D. and Pieri G. and Reggiannini M. and Pascali M.  A.},
	publisher = {IEEE, New York, USA},
	doi = {10.1109/itnt57377.2023.10139044},
	booktitle = {ITNT 2023 - IX International Conference on Information Technology and Nanotechnology, Samara, Russia, 17-21/04/2023},
	year = {2023}
}