Deep Learning–Based Identification of LULUCF Categories Using a Mixture of Experts Architecture and Remote Sensing Data (Congresos UEx, actas de Congresos, 2026)
In the context of climate policies, land use and land cover monitoring is a key component for assessing the LULUCF sector. In this regard, remote sensing combined with deep learning offers new opportunities for more consistent and spatially detailed mapping. The objective of this study is to evaluate the feasibility of a deep learning architecture based on a Mixture of Experts for the hierarchical identification of LULUCF categories, with a particular focus on forested areas. The proposed methodology relies on a cascaded architecture composed of three specialized U-Net models addressing contextual land-use classification, binary tree detection, and tree-species identification, using high-resolution SIOSE-AR data over an MTN50 sheet in northern Spain. Preliminary results indicate a coherent identification of spatial patterns and major land-use categories, although class imbalance remains a limiting factor. Overall, the approach shows strong potential for LULUCF monitoring using remote sensing data.