A refined SMR-DeepLabV3+ model for cultivated land extraction from cross-view imagery using an attention mechanism

The protection and monitoring of cultivated land, as a vital resource, are essential for ensuring food security and advancing sustainable agricultural development, directly contributing to the Sustainable Development Goals (SDGs). Rapid and accurate extraction of cultivated land significantly improves the quality and efficiency of comprehensive natural resource monitoring. In recent years, advanced technologies for cultivated land extraction, particularly deep learning-based semantic segmentation, have yielded impressive results. The DeepLabV3+ model has demonstrated exceptional performance across multiple datasets. However, conventional approaches primarily rely on high-resolution remote sensing imagery, which may not always be feasible in practical scenarios requiring cross-perspective image analysis (e.g., images captured by handheld devices or drones), such as in cultivated land survey verification. This paper introduces a new algorithm, SMR-DeepLabV3+, which integrates an attention mechanism into the DeepLabV3+ model, along with regularization enhancements. The model was trained using 88,268 meticulously annotated samples and evaluated on 3466 cultivated land patches from Zhaohua District, Guangyuan City, Sichuan Province. The results showed recognition rates of 80.3% for paddy fields, 81.7% for irrigated land, and 86.1% for dryland. The SMR-DeepLabV3+ model significantly improves the precision of cultivated land extraction and area estimation, offering a practical and effective solution for identifying different types of cultivated land. This study contributes to more refined management of cultivated land resources and provides reliable support for assessing the effectiveness of cultivated land protection policies.