The accurate and automated delineation of Field Parcels (FPs) serves as the foundation for modern precision agriculture. While deep learning-based extraction from high-resolution remote sensing imagery has improved pixel-level accuracy, current methods often neglect the intrinsic topological relationships between parcels, leading to geometric inconsistencies such as broken boundaries and structural ambiguities. To address these limitations, this paper proposes a topology-aware, end-to-end framework for polygonal FP extraction. We employed Convolutional Neural Networks (CNNs) with a coupled boundary-region representation to extract deep features that implicitly encode boundary width. Crucially, we introduce a Topological Relationship Construction (TRC) mechanism that transforms raster features into a node-edge topological network, enabling the direct generation of vector entities with guaranteed spatial adjacency. Based on this topology, we further developed Double-Line Detection (DLD) and Dangling Line Extension (DLE) algorithms to resolve the topological absence of single/double-line boundaries and fixed fracture errors in complex scenarios. Experimental results demonstrate that the proposed method achieved an F1 score of 0.910 and an IoU of 0.835, effectively ensuring stable and geometrically reasonable outputs even when CNN predictions are fragmented. This approach provides a solution for end-to-end vector mapping in agriculture.
Duan et al. (Mon,) studied this question.