Abstract: Accurate agricultural parcel extraction from remote sensing images for agricultural resource monitoring is a critical technology for achieving intelligent management of cultivated land resources. To address the insufficient segmentation accuracy caused by blurred boundaries, diverse textures, and morphological heterogeneity in complex farmland scenarios in existing deep learning methods, this paper proposes a multi-task neural network ESDNet featuring collaborative edge-semantic optimization. The model achieves performance improvements through three innovative mechanisms: Firstly, a coordinate attention (CA) module is embedded between the encoder and main decoder to enhance the discriminative capability for ambiguous boundaries through coordinate-sensitive attention weighting. Secondly, a feature enhancement (FE) module with multi-level receptive fields is designed, employing pyramid dilated convolutions and adaptive feature fusion strategies to improve the model's resolution of heterogeneous textures. Thirdly, a multi-task collaborative optimization framework inte- grating boundary mapping, distance mapping, and mask mapping is constructed, reinforcing spatial cognition of morphologically complex parcels via a joint learning strategy combining geometric constraints and semantic guidance. To validate the model's generalizability, experiments were conducted on multi-source remote sensing datasets (Gaofen-2 and Sentinel-2 imagery) covering Shandong and Sichuan regions in China and the Netherlands. Results demonstrate that ESDNet achieves superior performance, surpassing state-of-the-art models by 0.77 percentage points, 2.17 percentage points, and 2.28 percentage points in intersection over union (IoU) across the three regions, respectively. The model’s strong generalization capability and high-precision segmentation characteristics provide reliable technical support for dynamic monitoring of cultivated land resources in smart agriculture.

Key words: agricultural parcel extraction, remote sensing, semantic segmentation, neural network, multi-task learning