RT info:eu-repo/semantics/article
T1 Multi-frame cloud prediction in all-sky images from RGB images and segmented masks
A1 Gatón Herguedas, Javier
A1 Román Díez, Roberto
A1 Guzman, César
A1 González Fernández, Daniel
A1 Longarela, Bruno
A1 Toledano Olmeda, Carlos
A1 González Caton, Ramiro
K1 All-sky images
K1 Clouds
K1 Cloud motion prediction
K1 Artificial intelligence
K1 Semantic segmentation
K1 Next-frame prediction
K1 33 Ciencias Tecnológicas
AB This paper presents a comparative study on the impact of input representation on deterministic artificial intelligence models for short-term multi-frame prediction in all-sky images. This work compares a model operating on 8-bit RGB all-sky images with a model that shares the same backbone, but operates directly on semantically segmented masks that encode cloud-related classes. Using an available sky segmentation model, predictions are evaluated in the segmentation label space using segmenter-derived masks as a proxy reference. Within this evaluation framework, the use of semantic masks as input for short-term prediction leads to improved temporal stability and higher agreement across standard segmentation metrics such as intersection over union, Dice coefficient, and categorical cross-entropy. While these results suggest potential relevance for weather and solar energy nowcasting applications, further validation against physical irradiance measurements is required.
PB Elsevier
SN 0038-092X
YR 2026
FD 2026
LK https://uvadoc.uva.es/handle/10324/83808
UL https://uvadoc.uva.es/handle/10324/83808
LA eng
NO Solar Energy, 2026, vol. 311, p. 114515
NO Producción Científica
DS UVaDOC
RD 25-mar-2026