  Satellite missions and Earth Observation (EO) systems represent fundamental
assets for environmental monitoring and the timely identification of
catastrophic events, long-term monitoring of both natural resources and
human-made assets, such as vegetation, water bodies, forests as well as
buildings. Different EO missions enables the collection of information on
several spectral bandwidths, such as MODIS, Sentinel-1 and Sentinel-2. Thus,
given the recent advances of machine learning, computer vision and the
availability of labeled data, researchers demonstrated the feasibility and the
precision of land-use monitoring systems and remote sensing image
classification through the use of deep neural networks. Such systems may help
domain experts and governments in constant environmental monitoring, enabling
timely intervention in case of catastrophic events (e.g., forest wildfire in a
remote area). Despite the recent advances in the field of computer vision, many
works limit their analysis on Convolutional Neural Networks (CNNs) and, more
recently, to vision transformers (ViTs). Given the recent successes of Graph
Neural Networks (GNNs) on non-graph data, such as time-series and images, we
investigate the performances of a recent Vision GNN architecture (ViG) applied
to the task of land cover classification. The experimental results show that
ViG achieves state-of-the-art performances in multiclass and multilabel
classification contexts, surpassing both ViT and ResNet on large-scale
benchmarks.
