Study of various machine learning approaches for Sentinel-2 derived bathymetry
- DOI
- 10.1371/journal.pone.0291595
- Published
- 2023-09-15
- Container
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pone.0291595,
title = {Study of various machine learning approaches for Sentinel-2 derived bathymetry},
author = {Andrzej Chybicki and Paweł Sosnowski and Marek Kulawiak and Tomasz Bieliński and Waldemar Korlub and Zbigniew Łubniewski and Magdalena Kempa and Jarosław Parzuchowski},
year = {2023},
journal = {PLOS ONE},
doi = {10.1371/journal.pone.0291595},
url = {https://doi.org/10.1371/journal.pone.0291595}
}RIS
TY - JOUR TI - Study of various machine learning approaches for Sentinel-2 derived bathymetry AU - Andrzej Chybicki AU - Paweł Sosnowski AU - Marek Kulawiak AU - Tomasz Bieliński AU - Waldemar Korlub AU - Zbigniew Łubniewski AU - Magdalena Kempa AU - Jarosław Parzuchowski PY - 2023 JO - PLOS ONE DO - 10.1371/journal.pone.0291595 UR - https://doi.org/10.1371/journal.pone.0291595 ER -
APA
Chybicki, A., Sosnowski, P., Kulawiak, M., Bieliński, T., Korlub, W., Łubniewski, Z., Kempa, M., & Parzuchowski, J. (2023). Study of various machine learning approaches for Sentinel-2 derived bathymetry. PLOS ONE. https://doi.org/10.1371/journal.pone.0291595
Source records
- crossref · retrieved 2026-11-07T12:00:24.521Z