  Nowadays, there is a general agreement on the need to better characterize
agricultural monitoring systems in response to the global changes. Timely and
accurate land use/land cover mapping can support this vision by providing
useful information at fine scale. Here, a deep learning approach is proposed to
deal with multi-source land cover mapping at object level. The approach is
based on an extension of Recurrent Neural Network enriched via an attention
mechanism dedicated to multi-temporal data context. Moreover, a new
hierarchical pretraining strategy designed to exploit specific domain knowledge
available under hierarchical relationships within land cover classes is
introduced. Experiments carried out on the Reunion island - a french overseas
department - demonstrate the significance of the proposal compared to remote
sensing standard approaches for land cover mapping.
