  Maintaining farm sustainability through optimizing the agricultural
management practices helps build more planet-friendly environment. The emerging
satellite missions can acquire multi- and hyperspectral imagery which captures
more detailed spectral information concerning the scanned area, hence allows us
to benefit from subtle spectral features during the analysis process in
agricultural applications. We introduce an approach for extracting 2.5 m
cultivated land maps from 10 m Sentinel-2 multispectral image series which
benefits from a compact graph convolutional neural network. The experiments
indicate that our models not only outperform classical and deep machine
learning techniques through delivering higher-quality segmentation maps, but
also dramatically reduce the memory footprint when compared to U-Nets (almost
8k trainable parameters of our models, with up to 31M parameters of U-Nets).
Such memory frugality is pivotal in the missions which allow us to uplink a
model to the AI-powered satellite once it is in orbit, as sending large nets is
impossible due to the time constraints.
