Yongchao Shan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yongchao Shan has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Anqi Zhang
Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
1 shared publication - Chunjiang Zhao
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
1 shared publication - Lingyi An
Bayannur Lingda Modern Agriculture Development Co., Ltd., Bayannur 015000, China
1 shared publication - Weiqiang Fu
Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
1 shared publication - Xiaofei An
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
1 shared publication - Yanxin Yin
Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
1 shared publication
Latest Publications
- Open Access
- 1 July 2026
- Published by MDPI AG in Remote Sensing
- Vol. 18 (13) , 2130
Farm Topological Map Construction from Road Vector Data for Unmanned Farm Navigation
In unmanned farms, machinery transfer between fields and access to field entrances are essential prerequisites for autonomous field operations, and both require support from an accurately structured farm-road network. However, existing road data are typically maintained as vector layers and lack the topological relationships and geometric attributes needed for transfer-route and field-entrance planning. This study proposes a method for constructing farm-road topological maps from road and field vector data. The method converts road polygons into a node–edge graph containing centerline geometry, estimates road-segment widths to support the safe passage of agricultural machinery, and establishes bidirectional road–field associations based on field-access nodes. Experiments in three farm areas show that the proposed method achieves a mean symmetric centerline error of 0.094 m; width-estimation mean absolute error (MAE) and root mean square error (RMSE) of 0.032 m and 0.060 m, respectively; and a 100% success rate in 50 random path-planning tasks. The farm-road topological map constructed by this method provides spatial infrastructure for agricultural-machinery path planning and operation scheduling in unmanned farms.
- 1 July 2026
- Published by MDPI AG in Remote Sensing
- Vol. 18 (13) , 2130
Open AccessFarm Topological Map Construction from Road Vector Data for Unmanned Farm Navigation
In unmanned farms, machinery transfer between fields and access to field entrances are essential prerequisites for autonomous field operations, and both require support from an accurately structured farm-road network. However, existing road data are typically maintained as vector layers and lack the topological relationships and geometric attributes needed for transfer-route and field-entrance planning. This study proposes a method for constructing farm-road topological maps from road and field vector data. The method converts road polygons into a node–edge graph containing centerline geometry, estimates road-segment widths to support the safe passage of agricultural machinery, and establishes bidirectional road–field associations based on field-access nodes. Experiments in three farm areas show that the proposed method achieves a mean symmetric centerline error of 0.094 m; width-estimation mean absolute error (MAE) and root mean square error (RMSE) of 0.032 m and 0.060 m, respectively; and a 100% success rate in 50 random path-planning tasks. The farm-road topological map constructed by this method provides spatial infrastructure for agricultural-machinery path planning and operation scheduling in unmanned farms.