Logistic regression versus XGBoost for detecting burned areas using satellite images

Militino, Ana F.; Goyena Baroja, Harkaitz; Pérez Goya, Unai; Ugarte Martínez, María Dolores

Publication:
Logistic regression versus XGBoost for detecting burned areas using satellite images

dc.contributor.author	Militino, Ana F.
dc.contributor.author	Goyena Baroja, Harkaitz
dc.contributor.author	Pérez Goya, Unai
dc.contributor.author	Ugarte Martínez, María Dolores
dc.contributor.department	Estadística, Informática y Matemáticas	es_ES
dc.contributor.department	Estatistika, Informatika eta Matematika	eu
dc.contributor.department	Institute for Advanced Materials and Mathematics - INAMAT2	en
dc.contributor.funder	Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa	es
dc.date.accessioned	2024-04-03T17:23:32Z
dc.date.available	2024-04-03T17:23:32Z
dc.date.issued	2024
dc.date.updated	2024-04-03T17:09:14Z
dc.description.abstract	Classical statistical methods prove advantageous for small datasets, whereas machine learning algorithms can excel with larger datasets. Our paper challenges this conventional wisdom by addressing a highly significant problem: the identification of burned areas through satellite imagery, that is a clear example of imbalanced data. The methods are illustrated in the North-Central Portugal and the North-West of Spain in October 2017 within a multi-temporal setting of satellite imagery. Daily satellite images are taken from Moderate Resolution Imaging Spectroradiometer (MODIS) products. Our analysis shows that a classical Logistic regression (LR) model competes on par, if not surpasses, a widely employed machine learning algorithm called the extreme gradient boosting algorithm (XGBoost) within this particular domain.	en
dc.description.sponsorship	Open Access funding provided by Universidad Pública de Navarra. This work has been funded by the project PID2020-113125RB-I00 of the Spanish Research Agency (MCIN/ AEI/10.13039/501100011033) and Ayudas predoctorales UPNA 2022-2023.	en
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Militino, A. F., Goyena, H., Pérez-Goya, U., & Ugarte, M. D. (2024). Logistic regression versus XGBoost for detecting burned areas using satellite images. Environmental and Ecological Statistics, 31(1), 57-77. https://doi.org/10.1007/s10651-023-00590-7	en
dc.identifier.doi	10.1007/s10651-023-00590-7
dc.identifier.issn	1352-8505
dc.identifier.uri	https://academica-e.unavarra.es/handle/2454/47827
dc.language.iso	eng	en
dc.publisher	Springer	en
dc.relation.ispartof	Environmental and Ecological Statistics (2024), 31(1), 57–77	en
dc.relation.projectID	info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113125RB-I00/ES/	en
dc.relation.publisherversion	https://doi.org/10.1007/s10651-023-00590-7
dc.rights	© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License.	en
dc.rights.accessRights	Acceso abierto / Sarbide irekia	es
dc.rights.accessRights	info:eu-repo/semantics/openAccess	en
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.subject	Commission error	en
dc.subject	LR	en
dc.subject	Machine learning	en
dc.subject	MODIS	en
dc.subject	Omission error	en
dc.subject	Spectral indices	en
dc.subject	VIIRS	en
dc.subject	XGBoost	en
dc.title	Logistic regression versus XGBoost for detecting burned areas using satellite images	en
dc.type	info:eu-repo/semantics/article
dc.type.version	Versión publicada / Argitaratu den bertsioa	es
dc.type.version	info:eu-repo/semantics/publishedVersion	en
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