An Automatic Approach for the Production of a Time Series of Consistent Land-Cover Maps Based on Long-Short Term Memory

This paper presents an approach that aims to produce a Time-Series (TS) of consistent Land-Cover (LC) maps, typically needed to perform environmental monitoring. First, it creates an annual training set for each TS to be classified, leveraging on publicly available thematic products. These annual training sets are then used to generate a set of preliminary LC maps that allow for the identification of the unchanged areas, i.e., the stable temporal component. Such areas can be used to define an informative and reliable multi-year training set, by selecting samples belonging to the different years for all the classes. The multi-year training set is finally employed to train a unique multi-year Long Short Term Mem-ory (LSTM) model, which enhances the consistency of the annual LC maps. The preliminary results carried out on three TSs of Sentinel 2 images acquired in Italy in 2018,2019 and 2020 demonstrates the capability of the method to improve the consistency of the annual LC maps. The agreement of the obtained maps is $\approx 78{\%}$, compared to the $\approx 74{\%}$ achieved by the LSTM models trained separately.