发表机构
University of Twente; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing（特文特大学; 测绘遥感信息工程国家重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对农业地块矢量化中拓扑冲突与共享边界缺失问题，提出语义引导扩散与拓扑感知多边形化框架，实现零侵入率与高共享边界召回率。
AI 中文摘要
农业地块多边形在精准农业、土地管理和作物监测等地理空间应用中发挥着基础作用。除了规则的多边形几何形状和低顶点冗余度外，实际的地块图还应避免拓扑冲突，并保留相邻田地之间的公共边界。然而，这一要求在很大程度上仍未得到解决：基于分割的方法主要生成地块掩膜或栅格边界线索，并依赖启发式的栅格到矢量转换；基于实例和基于轮廓的方法独立重建地块；而近期面向矢量的方法虽然提高了多边形规则性，但并未从共享拓扑结构中显式恢复相邻地块。为解决这一不足，我们提出了一种用于农业地块拓扑一致矢量化的语义引导扩散框架。该框架将联合边-顶点潜空间扩散与有监督的多线索条件化相结合，以生成几何规则化的地块边界和顶点基元，同时抑制假阳性响应。随后，一种拓扑感知的地块多边形重建方法通过从公共平面图中重建地块面，将这些基元转换为规则多边形，使相邻的预测地块能够复用共享边界并避免相互内部侵入。在AI4SmallFarms和iFLYTEK数据集上的大量实验从像素级覆盖率、几何保真度、对象级正确性和拓扑一致性方面评估了地块矢量化性能。结果表明，该方法表现出强劲且具有竞争力的性能，实现了零测量侵入率和最高的共享边界召回率，展示了所提框架在实现准确、规则且拓扑一致的农业地块矢量化方面的潜力。
英文摘要
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.
