A lightweight hybrid ViT-GNN framework for data-centric land cover mapping in the amazon biome using graph structural priors
Year: 2026
Type: article
Abstract: 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 of extracted color-based vegetation indices (e.g., GRVI) and textural statistics. The dual-branch design isolates global sequence context via the ViT module while leveraging the GNN branch as a structural prior to learn non-linear covariance between specific features. Evaluated against a suite of benchmarks including MiniViT, Baseline CNN, Random Forest, XGBoost, and LightGBM, the proposed algorithm attained the highest Overall Accuracy of 0.930 utilizing merely 16,323 trainable parameters-a nearly 75% reduction in footprint versus pixel-based models. Crucially, a McNemar's statistical test confirmed that the accuracy gain over the strongest classical baseline (XGBoost, OA: 0.927) is statistically significant (p < 0.05). By pairing rigorous spatial cross-validation with an interpretable feature space, this work establishes that intelligent data pre-processing combined with graph-based relational learning offers a robust framework for high-precision environmental mapping under severe resource limitations.
Source: Scientific Reports
Institution: Muria Kudus University
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
SDG: Life on land
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7165925994
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