  In this article, we investigate several structured deep learning models for
crop type classification on multi-spectral time series. In particular, our aim
is to assess the respective importance of spatial and temporal structures in
such data. With this objective, we consider several designs of convolutional,
recurrent, and hybrid neural networks, and assess their performance on a large
dataset of freely available Sentinel-2 imagery. We find that the
best-performing approaches are hybrid configurations for which most of the
parameters (up to 90%) are allocated to modeling the temporal structure of the
data. Our results thus constitute a set of guidelines for the design of bespoke
deep learning models for crop type classification.
