With the advent of modern Earth Observation (EO) systems, the opportunity of collecting Satellite Image Time Series (SITS) provides valuable insights to monitor spatio-temporal dynamics. Within this context, accurate Land Use/Land Cover (LULC) mapping plays a pivotal role in supporting territo- rial management and facilitating informed decision-making processes. However, traditional pixel-based and object-based classification methods often face challenges to effectively exploit spectral and spatial information. In this study, we propose Orthrus, a novel approach that fuses multi-scale information for enhanced LULC mapping. The proposed approach exploits several 2D encoding techniques to encode times series information into imagery. The resulting image is leveraged as input to a standard Convo- lutional Neural Network (CNN) image classifier to cope with the downstream classification task. The evaluations on two real word benchmarks namely, Dordogne and Reunion-Island, demonstrated the qual- ity of Orthrus over state-of-the-art techniques from the field of land cover mapping based on SITS data. More precisely, Orthrus exhibits an enhancement of more than 3.5 accuracy points compared to the best competing approach on the Dordogne benchmark, and surpasses the best competing approach on the Reunion-Island dataset by over 3 accuracy points.