TopoFarm: A Topology-Annotated Panoptic Dataset for Unauthorized Farmland Excavation Scene Representation

Unauthorized farmland excavation is a prominent manifestation of farmland non-agriculturalization, and its effective monitoring depends on structured representations of objects and their spatial interactions in complex scenes. However, the existing computer vision research mainly focuses on object-level recognition or scene-level classification, while lacking datasets that explicitly model topological relationships in farmland excavation scenarios. To address this limitation, this paper presents TopoFarm, a topology-annotated panoptic dataset for unauthorized farmland excavation scenes. TopoFarm provides fine-grained panoptic segmentation annotations together with pairwise object contact relationship labels, enabling joint object–relation modeling and topology-aware scene representation. To improve annotation reliability under complex conditions, a human-in-the-loop hybrid intelligence framework, termed HITPA, is introduced to integrate automatic panoptic segmentation, depth-aware topological reasoning, and expert-guided refinement, achieving high annotation quality with controlled manual effort. Based on TopoFarm, systematic benchmark experiments are conducted for panoptic segmentation and topological relationship reasoning, along with a hierarchical evaluation protocol to analyze the impact of object-level representation quality on relational inference. The results demonstrate that TopoFarm poses substantial challenges for both tasks and highlight the strong dependence of topological reasoning on object accuracy and global scene context. Overall, TopoFarm provides a new data foundation and evaluation benchmark for topology-aware perception in farmland monitoring applications.