Land Cover (LC) maps generated by the classification of Remote Sensing (RS) data allow for the monitoring of Earth processes and the dynamics of objects and phenomena. Environmental monitoring applications can implement accurate quantification of LC variability when maps are spatiotemporally consistent and are continuously updated, as they provide information on consistent and permanent LC changes. However, the production of frequent and spatiotemporally consistent LC maps is challenging because it involves balancing the need for temporal consistency with the risk of missing real changes. In this work, we propose a scalable and semi-automatic method for generating annual maps with labels that are consistently applied from one year to the next. It uses a Transformer Deep Learning (DL) model as a classifier, which is trained on satellite Time Series (TS) data using High Performance Computing (HPC). The trained model is able to generate stable land cover maps by shifting the prediction window along the temporal direction. We test the method on a Sentinel-2 dataset acquired over a three-year period and demonstrate that: (1) the annual maps can be directly compared to detect changes and, (2) the accuracy of previously generated maps can be improved via a backpropagation strategy.
