@article {aMederos-Barrera25,
	title = {Comparison of Conventional Machine Learning and Convolutional Deep Learning models for Seagrass Mapping using Satellite Imagery},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
	year = {2025},
	month = {12/2025},
	pages = {1-19},
	chapter = {1},
	abstract = {<p><span style="color: $\#$333333; font-family: {\textquoteright}HelveticaNeue Regular{\textquoteright}, sans-serif; font-size: 18px;">Benthic communities, such as seagrass meadows, play a crucial environmental role in marine ecosystems and provide socio-economic benefits. Satellite remote sensing is currently used for their monitoring, and Deep Learning (DL) techniques offer improvements in mapping quality compared to traditional Machine Learning (ML). This study compares conventional ML and convolutional DL models for mapping Cymodocea nodosa meadows in El R{\'\i}o, Canary Islands, using WorldView-2 satellite imagery. An in-situ measurement campaign was conducted to generate an open dataset for segmentation. Evaluated models include Decision Trees, Gaussian Na{\"\i}ve Bayes, Support Vector Machines, K-Nearest Neighbors, Subspace KNN, Feedforward Neural Networks, U-Net, Attention U-Net, and Pix2Pix models. Results show that DL models significantly outperform conventional ML models in detecting Cymodocea nodosa. The best model (U-net) achieved an Intersection over Union (IoU) of 83\% overall and 74\% for Cymodocea nodosa, while the best ML model (FNN) only reached 62\% and 23\%, respectively. IoU was highlighted for its sensitivity to minor mapping changes. Additionally, a temporal analysis revealed a dramatic 96\% reduction in Cymodocea nodosa coverage over 21 years, from 245.32 ha in 2001 to 9.31 ha in 2022. This study not only compares conventional ML and convolutional DL techniques for benthic habitat mapping but also provides a valuable methodology and dataset for future marine ecosystem monitoring research.</span></p>},
	keywords = {Cymodocea nodosa, deep learning, machine learning, seagrass mapping},
	issn = {2151-1535},
	doi = {10.1109/JSTARS.2025.3642923},
	url = {https://ieeexplore.ieee.org/document/11297818},
	author = {Antonio Mederos-Barrera and Laia Albors and Ferran Marques and Javier Marcello and Gerard Martinez and Francisco Eugenio}
}
