Jiadong Zhang

To address the limited automation of 3D reconstruction caused by the inability of point clouds to represent the hidden structures and construction logic of Ming–Qing large timber buildings, this study proposes a hidden structural parameter inference method that integrates point clouds with traditional construction rules. First, a unified parameter space is established to provide a structured representation of building components and their associated parameters. Second, traditional construction knowledge is formalized into computable proportional, relational, and spatial constraints. Finally, hidden structural parameters are inferred through hierarchical constraint propagation, and the inferred results are used to generate HBIM components. The proposed method was validated using the sub-eave columns and their associated components of the Dabei Hall of Chongshan Temple in Taiyuan. Among 1848 hidden structural parameters, 1800 were successfully inferred, corresponding to a solvability rate of 97.4%. The results demonstrate that the proposed method can effectively infer hidden structural parameters under the available observations and construction-rule constraints and use the rule-consistent inference results to generate parametric HBIM components. This study extends HBIM beyond geometric representation toward knowledge-driven model representation, providing a knowledge-enhanced modeling approach for the digital documentation and structural understanding of traditional timber architecture.

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.

Map annotation interpretation is crucial for geographic information extraction and intelligent map analysis. This study addresses the challenges associated with interpreting Chinese map annotations, specifically visual complexity and data scarcity issues, by proposing a hybrid intelligence-based multi-source unstructured Chinese map annotation interpretation method (HI-CMAIM). Firstly, leveraging expert knowledge in an innovative way, we constructed a high-quality expert knowledge-based map annotation dataset (EKMAD), which significantly enhanced data diversity and accuracy. Furthermore, an improved annotation detection model (CMA-DB) and an improved annotation recognition model (CMA-CRNN) were designed based on the characteristics of map annotations, both incorporating expert knowledge. A two-stage transfer learning strategy was employed to tackle the issue of limited training samples. Experimental results demonstrated the superiority of HI-CMAIM over existing algorithms. In the detection task, CMA-DB achieved an 8.54% improvement in Hmean (from 87.73% to 96.27%) compared to the DB algorithm. In the recognition task, CMA-CRNN achieved a 15.54% improvement in accuracy (from 79.77% to 95.31%) and a 4-fold reduction in NED (from 0.1026 to 0.0242), confirming the effectiveness and advancement of the proposed method. This research not only provides a novel approach and data support for Chinese map annotation interpretation but also fills the gap of high-quality, diverse datasets. It holds practical application value in fields such as geographic information systems and cartography, significantly contributing to the advancement of intelligent map interpretation.