Accurate mapping method keeps farm field borders neat and shared
Topologically Consistent Agricultural Parcel Vectorization with Semantic-Guided Diffusion and Topology-Aware Polygonization
Computer Vision and Pattern Recognition
Summary
Farm fields need clear boundaries on maps to help with farming and land management. Existing methods often create messy borders or don’t make sure that neighboring fields share the same border line, causing overlap or gaps. The authors developed a new way that uses a special computer technique called semantic-guided diffusion to create smooth lines and points representing field edges. Then, their method carefully reconstructs the fields to share boundaries without overlapping. Tests show this method makes very accurate and consistent farm maps that avoid common errors.
agricultural parcelsgeospatial mappingpolygon vectorizationtopological consistencysemantic-guided diffusionlatent diffusionpolygon reconstructionplanar graphshared boundariesprecision agriculture
Authors
Weiqin Jiao, Xiaolong Zuo, Claudio Persello
Abstract
Agricultural parcel polygons play a fundamental role in geospatial applications such as precision agriculture, land administration, and crop monitoring. Beyond regular polygon geometry and low vertex redundancy, practical parcel maps should avoid topological conflicts and preserve common boundaries between adjacent fields. Yet this requirement remains largely unresolved: segmentation-based methods mainly produce parcel masks or raster boundary cues and rely on heuristic raster-to-vector conversion, instance- and contour-based methods reconstruct parcels independently, and recent vector-oriented methods improve polygon regularity but do not explicitly recover adjacent parcels from a shared topological structure. To address this gap, we propose a semantic-guided diffusion framework for topologically consistent agricultural parcel vectorization. It couples joint edge--vertex latent diffusion with supervised multi-cue conditioning to generate geometrically regularised parcel-boundary and vertex primitives while suppressing false-positive responses. A topology-aware parcel polygon reconstruction method then converts these primitives into regular polygons by reconstructing parcel faces from a common planar graph, enabling adjacent predicted parcels to reuse shared boundaries and avoid mutual interior intrusion. Extensive experiments on the AI4SmallFarms and iFLYTEK datasets evaluate parcel vectorization in terms of pixel-level coverage, geometric fidelity, object-level correctness, and topological consistency. The results show strong and competitive performance, with zero measured intrusion ratio and the highest shared-edge recall, demonstrating the potential of the proposed framework for accurate, regular, and topologically consistent agricultural parcel vectorization.