Publications

Militino, AF; Goyena, H; Pérez-Goya, U; Ugarte, MD (2024). Logistic regression versus XGBoost for detecting burned areas using satellite images. ENVIRONMENTAL AND ECOLOGICAL STATISTICS.

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:
1573-3009

ISSN:
10.1007/s10651-023-00590-7