The advent of multitemporal high resolution data, like theCopernicus Sentinel-2, has enhanced significantly the poten-tial of monitoring the earth’s surface and environmental dy-namics. In this paper, we present a novel deep learning frame-work for urban change detection which combines state-of-the-art fully convolutional networks (similar to U-Net) forfeature representation and powerful recurrent networks (suchas LSTMs) for temporal modeling. We report our resultson the recently publicly available bi-temporal Onera Satel-lite Change Detection (OSCD) Sentinel-2 dataset, enhancingthe temporal information with additional images of the sameregion on different dates. Moreover, we evaluate the perfor-mance of the recurrent networks as well as the use of the ad-ditional dates on the unseen test-set using an ensemble cross-validation strategy. All the developed models during the val-idation phase have scored an overall accuracy of more than95%, while the use of LSTMs and further temporal informa-tion, boost the F1 rate of the change class by an additional 1,5%.