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.

卫星任务和地球观测系统对于环境监测和及时发现灾难性事件以及长期监测自然资源和人造资产如植被、水体、森林和建筑物方面具有基础性作用。近期机器学习、计算机视觉的进展以及标注数据的可用性使得研究人员通过使用深度神经网络展示了在土地利用监测系统和遥感图像分类方面的可行性和精确性。该研究调查了一种最新的视觉图神经网络架构（ViG）在土地覆盖分类任务中的性能，并实验结果显示 ViG 在多类别和多标签分类背景下的表现超过了 ViT 和 ResNet 在大规模基准测试中的最新水平。