Composing and inverting cardinal direction relations with the 3D-CRN model
Hao Tang, Miao Wang, Weiguang Liu, Zhenxi Fang, Yanfei Zhu, Yu Wang · Journal of Spatial Science · 2025
As spatial data applications grow increasingly complex, representing 3D directions has become critical. Existing models suffer from inaccuracies and overlook object details. The 3D-CRN model addresses these issues by integrating directional relationships and qualitative distances. It proposes an automatic algorithm for composite reasoning of cardinal directions. Additionally, by combining 3D algebra with qualitative coordinates, an equivalence relationship is established, and an inverse-calculation algorithm is developed. This approach eliminates the need for manual reasoning tables, enhancing efficiency and practicality. Theoretical analysis demonstrates improved accuracy alongside reduced time complexity.