  The availability of massive earth observing satellite data provide huge
opportunities for land use and land cover mapping. However, such mapping effort
is challenging due to the existence of various land cover classes, noisy data,
and the lack of proper labels. Also, each land cover class typically has its
own unique temporal pattern and can be identified only during certain periods.
In this article, we introduce a novel architecture that incorporates the UNet
structure with Bidirectional LSTM and Attention mechanism to jointly exploit
the spatial and temporal nature of satellite data and to better identify the
unique temporal patterns of each land cover. We evaluate this method for
mapping crops in multiple regions over the world. We compare our method with
other state-of-the-art methods both quantitatively and qualitatively on two
real-world datasets which involve multiple land cover classes. We also
visualise the attention weights to study its effectiveness in mitigating noise
and identifying discriminative time period.
