Viewpoint Selection for 3D Scenes in Map Narratives
Narrative mapping, an advanced geographic information visualization technology, presents spatial information episodically, enhancing readers’ spatial understanding and event cognition. However, during 3D scene construction, viewpoint selection is heavily reliant on the cartographer’s subjective inte...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/6/219 |
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| _version_ | 1849432879229042688 |
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| author | Shichuan Liu Yong Wang Qing Tang Yaoyao Han |
| author_facet | Shichuan Liu Yong Wang Qing Tang Yaoyao Han |
| author_sort | Shichuan Liu |
| collection | DOAJ |
| description | Narrative mapping, an advanced geographic information visualization technology, presents spatial information episodically, enhancing readers’ spatial understanding and event cognition. However, during 3D scene construction, viewpoint selection is heavily reliant on the cartographer’s subjective interpretation of the event. Even with fixed-angle settings, the task of ensuring that selected viewpoints align with the narrative theme remains challenging. To address this, an automated viewpoint selection method constrained by narrative relevance and visual information is proposed. Narrative relevance is determined by calculating spatial distances between each element and the thematic element within the scene. Visual information is quantified by assessing the visual salience of elements as the ratio of their projected area on the view window to their total area. Pearson’s correlation coefficient is used to evaluate the relationship between visual salience and narrative relevance, serving as a constraint to construct a viewpoint fitness function that integrates the visual salience of the convex polyhedron enclosing the scene. The chaotic particle swarm optimization (CPSO) algorithm is utilized to locate the viewpoint position while maximizing the fitness function, identifying a viewpoint meeting narrative and visual salience requirements. Experimental results indicate that, compared to the maximum projected area method and fixed-value method, a higher viewpoint fitness is achieved by this approach. The narrative views generated by this method were positively recognized by approximately two-thirds of invited professionals. This process aligns effectively with narrative visualization needs, enhances 3D narrative map creation efficiency, and offers a robust strategy for viewpoint selection in 3D scene-based narrative mapping. |
| format | Article |
| id | doaj-art-a14f79437a4d4df3b0f1f07965224d1f |
| institution | Kabale University |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-a14f79437a4d4df3b0f1f07965224d1f2025-08-20T03:27:14ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-05-0114621910.3390/ijgi14060219Viewpoint Selection for 3D Scenes in Map NarrativesShichuan Liu0Yong Wang1Qing Tang2Yaoyao Han3Research Centre of Geo-Spatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100036, ChinaResearch Centre of Geo-Spatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100036, ChinaResearch Centre of Geo-Spatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100036, ChinaSchool of Spatial Informatics and Geomatics Engineering, Anhui University of Science and Technology, Huainan 232001, ChinaNarrative mapping, an advanced geographic information visualization technology, presents spatial information episodically, enhancing readers’ spatial understanding and event cognition. However, during 3D scene construction, viewpoint selection is heavily reliant on the cartographer’s subjective interpretation of the event. Even with fixed-angle settings, the task of ensuring that selected viewpoints align with the narrative theme remains challenging. To address this, an automated viewpoint selection method constrained by narrative relevance and visual information is proposed. Narrative relevance is determined by calculating spatial distances between each element and the thematic element within the scene. Visual information is quantified by assessing the visual salience of elements as the ratio of their projected area on the view window to their total area. Pearson’s correlation coefficient is used to evaluate the relationship between visual salience and narrative relevance, serving as a constraint to construct a viewpoint fitness function that integrates the visual salience of the convex polyhedron enclosing the scene. The chaotic particle swarm optimization (CPSO) algorithm is utilized to locate the viewpoint position while maximizing the fitness function, identifying a viewpoint meeting narrative and visual salience requirements. Experimental results indicate that, compared to the maximum projected area method and fixed-value method, a higher viewpoint fitness is achieved by this approach. The narrative views generated by this method were positively recognized by approximately two-thirds of invited professionals. This process aligns effectively with narrative visualization needs, enhances 3D narrative map creation efficiency, and offers a robust strategy for viewpoint selection in 3D scene-based narrative mapping.https://www.mdpi.com/2220-9964/14/6/219narrative map3D scenesviewpoint selectionviewpoint optimizationparticle swarm optimization |
| spellingShingle | Shichuan Liu Yong Wang Qing Tang Yaoyao Han Viewpoint Selection for 3D Scenes in Map Narratives ISPRS International Journal of Geo-Information narrative map 3D scenes viewpoint selection viewpoint optimization particle swarm optimization |
| title | Viewpoint Selection for 3D Scenes in Map Narratives |
| title_full | Viewpoint Selection for 3D Scenes in Map Narratives |
| title_fullStr | Viewpoint Selection for 3D Scenes in Map Narratives |
| title_full_unstemmed | Viewpoint Selection for 3D Scenes in Map Narratives |
| title_short | Viewpoint Selection for 3D Scenes in Map Narratives |
| title_sort | viewpoint selection for 3d scenes in map narratives |
| topic | narrative map 3D scenes viewpoint selection viewpoint optimization particle swarm optimization |
| url | https://www.mdpi.com/2220-9964/14/6/219 |
| work_keys_str_mv | AT shichuanliu viewpointselectionfor3dscenesinmapnarratives AT yongwang viewpointselectionfor3dscenesinmapnarratives AT qingtang viewpointselectionfor3dscenesinmapnarratives AT yaoyaohan viewpointselectionfor3dscenesinmapnarratives |