Paper
28 January 2025 Research on the expression method of we-map symbolic traditional villages based on object importance
Zechang Li, Shunhua Liao, Yaodong Yang, Wangzhang Zhu, Huabin Zhang
Author Affiliations +
Proceedings Volume 13506, Sixth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2024); 135062R (2025) https://doi.org/10.1117/12.3057511
Event: Sixth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2024), 2024, Qingdao, China
Abstract
Aiming at the problem that key objects with cultural features cannot be prominently expressed in the construction of traditional village scenes, this paper proposes a We-map symbolic traditional village expression method based on object importance. The approach utilizes the object importance assessment model as its theoretical foundation, determining the semantic significance of the object by considering the cultural characteristics of geographical objects in the village scene. Additionally, it integrates spatial three elements to establish the importance assessment model of the object. The evaluation results are then symbolized based on the We-map concept, with objects of high importance expressed preferentially through abstract symbols and those of secondary importance through simple symbols. Compared with the traditional loading method, the number of data transmissions per second is improved by 29 times, and the scene loading speed is improved by 10-15 seconds. The findings indicate that the approach has a significant positive impact on enhancing the productivity of constructing traditional village scenes, while also emphasizing the cultural attributes of important elements within the depicted scene.
(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Zechang Li, Shunhua Liao, Yaodong Yang, Wangzhang Zhu, and Huabin Zhang "Research on the expression method of we-map symbolic traditional villages based on object importance", Proc. SPIE 13506, Sixth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2024), 135062R (28 January 2025); https://doi.org/10.1117/12.3057511
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