Publications

Militino, AF; Goyena, H; P?rez-Goya, U; Ugarte, MD (2024). Logistic regression versus XGBoost for detecting burned areas using satellite images. ENVIRONMENTAL AND ECOLOGICAL STATISTICS, 31(1), 57-77.

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.

DOI:
10.1007/s10651-023-00590-7

ISSN:
1573-3009