Graph Neural Networks Extract High-Resolution Cultivated Land Maps From Sentinel-2 Image Series

Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment. The emerging satellite missions can acquire multispectral 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 (S-2) multispectral image (MSI) 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 31-M parameters of U-Nets). Such memory frugality is pivotal in the missions which allow us to uplink a model to the artificial intelligence (AI)-powered satellite once it is in orbit, as sending large nets is impossible due to the time constraints.