A Comprehensive Deep-Learning Framework for Fine-Grained Farmland Mapping From High-Resolution Images

Abstract

The extraction of large-scale farmland is essential for optimizing agricultural production and advancing sustainable development. To meet the urgent need for efficient farmland extraction and overcome existing technical challenges, we have developed a comprehensive farmland mapping framework that integrates advanced data, methodology, and cartographic techniques. Regarding data, we present the fine-grained farmland dataset (FGFD), which compiles high-quality, meticulously annotated very high-resolution (VHR) satellite images and captures distinct regional characteristics across eastern, southern, western, northern, and central China. Building on the FGFD, we propose the dual-branch boundary-aware network (DBBANet), which employs ResNet-50 as the encoder to extract multilayer encoded features and introduces two parallel decoding branches: a spatial-aware branch and a boundary-aware branch. The dual-branch architecture leverages both unique semantic information relevant to farmland and detailed boundary information, facilitating a more comprehensive and accurate representation of farmland areas. By combining this dataset with our innovative methodology, we further propose a farmland mapping framework designed for large-scale applications. The proposed framework enables the direct generation of high-precision vector maps from VHR images, providing crucial technical support for farmland management, resource assessment, and agricultural planning. Extensive experiments conducted on the FGFD have established benchmarks for 13 segmentation methods, demonstrating the state-of-the-art (SOTA) performance of our approach. In practical large-scale applications, our mapping framework produces high-precision vector maps with clear boundaries, bridging the gap in fine-grained farmland mapping and paving the way for further research and applications in this field. The source code of the proposed DBBANet and FGFD is available at: https://github.com/Henryjiepanli/DBBANet .