Predicting Cropland Non-Agriculturalization Susceptibility Using Multi-Source Data and Graph Attention Networks: A Case Study of Wuhan, China

Cropland non-agriculturalization (CNA) threatens food security, ecosystem services, and sustainable development amid accelerating global urbanization. However, existing monitoring methods are often retrospective and lack adequate spatial and temporal resolution for proactive management. This study proposes GS-GAT, a graph-based deep learning framework for predicting CNA susceptibility at the meso-spatial scale. A spatial graph was constructed for the non-central districts of Wuhan, China, and multisource features were extracted across four dimensions: imagery, land cover, topography, and socioeconomics. A comprehensive intensity index is developed to compute susceptibility levels at the street-block level based on multi-year land use data from 2018 to 2022. To address class imbalance, GraphSMOTE is employed to enhance minority node representation. The key model of GS-GAT is trained across four temporal snapshots using attention-based feature aggregation and joint optimization of classification and structural reconstruction losses. Experimental results show that GS-GAT demonstrated an average AUC of 85.6% and an F1 score of 82.6%, which increased to 93% and 91%, respectively, under relaxed evaluation criteria, whereby baseline models such as SVM and XGBoost were outperformed. Ablation studies confirm the contributions of feature fusion and GraphSMOTE to model robustness and minority class detection. The proposed framework offers a scalable and interpretable approach for early identification of cropland conversion risks, supporting more targeted land-use management and cropland protection strategies.