  Nowadays, modern Earth Observation systems continuously generate huge amounts
of data. A notable example is represented by the Sentinel-2 mission, which
provides images at high spatial resolution (up to 10m) with high temporal
revisit period (every 5 days), which can be organized in Satellite Image Time
Series (SITS). While the use of SITS has been proved to be beneficial in the
context of Land Use/Land Cover (LULC) map generation, unfortunately, machine
learning approaches commonly leveraged in remote sensing field fail to take
advantage of spatio-temporal dependencies present in such data.
  Recently, new generation deep learning methods allowed to significantly
advance research in this field. These approaches have generally focused on a
single type of neural network, i.e., Convolutional Neural Networks (CNNs) or
Recurrent Neural Networks (RNNs), which model different but complementary
information: spatial autocorrelation (CNNs) and temporal dependencies (RNNs).
In this work, we propose the first deep learning architecture for the analysis
of SITS data, namely \method{} (DUal view Point deep Learning architecture for
time series classificatiOn), that combines Convolutional and Recurrent neural
networks to exploit their complementarity. Our hypothesis is that, since CNNs
and RNNs capture different aspects of the data, a combination of both models
would produce a more diverse and complete representation of the information for
the underlying land cover classification task. Experiments carried out on two
study sites characterized by different land cover characteristics (i.e., the
\textit{Gard} site in France and the \textit{Reunion Island} in the Indian
Ocean), demonstrate the significance of our proposal.
