The widespread availability of satellite imagery data has enabled advancements in Land Use/Land Cover (LULC) and Urban Fabric (UF) mapping through deep learning. However, maintaining up-to-date urban land cover maps is challenged by the high cost and operational constraints of continuous field data collection. This study explores the feasibility of updating urban LULC maps using SITS-based semantic segmentation models trained on historical data, specifically examining a transfer scenario where a model trained on 2015 data is applied to 2020 imagery. We benchmark the performance of two convolution-based architectures (Unet and Unet3D), plus a recent spatio-temporal transformer-based approach (TSViT) and a proposed variant, named TSViT+SW, which incorporates a shifted window attention scheme. Experimental evaluations covering the urban area of Lyon, France, reveal that the proposed TSViT+SW model achieves the best results among transferred models, minimizing performance degradation compared to the ideal in-year training scenario. This work offers insights into the potential and limitations of using historical data to update urban land cover in the absence of fresh labeled data.