A lightweight hybrid ViT-GNN framework for data-centric land cover mapping in the amazon biome using graph structural priors (opens in new tab)

Continuous monitoring of the Amazon biome demands land cover classification models that are both highly sensitive and computationally feasible. To resolve the inherent trade-off between architectural complexity and predictive performance in spatial deep learning, this study introduces the Vision Transformer–Graph Neural Network with Feature Adaptation (ViT-GNN RFFA). In contrast to conventional end-to-end pixel models, this hybrid architecture operates exclusively on an 11-dimensional vector ...