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Article

Geometric Similarity Measurement Method for Micro Scene Generalization

by 1,2,3, 1,2,3,*, 1,2,3 and 1,2,3
1
Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
2
National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
3
Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
*
Author to whom correspondence should be addressed.
Academic Editors: Sanda Roșca, Ștefan Bilașco, Dănuț Petrea, Titus Cristian Man and Paul Sestraș
Appl. Sci. 2022, 12(2), 628; https://doi.org/10.3390/app12020628
Received: 6 December 2021 / Revised: 28 December 2021 / Accepted: 29 December 2021 / Published: 10 January 2022
(This article belongs to the Special Issue GIS Methods, Models and Applications in Interdisciplinary Studies)
Geometric similarity plays an important role in geographic information retrieval, map matching, and data updating. Many approaches have been developed to calculate the similarity between simple features. However, complex group objects are common in map and spatial database systems. With a micro scene that contains different types of geographic features, calculating similarity is difficult. In addition, few studies have paid attention to the changes in a scene’s geometric similarity in the process of generalization. In this study, we developed a method for measuring the geometric similarity of micro scene generalization based on shape, direction, and position. We calculated shape similarity using the hybrid feature description, and we constructed a direction Voronoi diagram and a position graph to measure the direction similarity and position similarity. The experiments involved similarity calculation and quality evaluation to verify the usability and effectiveness of the proposed method. The experiments showed that this approach can be used to effectively measure the geometric similarity between micro scenes. Moreover, the proposed method accounts for the relationships amongst the geometrical shape, direction, and position of micro scenes during cartographic generalization. The simplification operation leads to obvious changes in position similarity, whereas delete and merge operations lead to changes in direction and position similarity. In the process of generalization, the river + islands scene changed mainly in shape and position, the similarity change in river + lakes occurred due to the direction and location, and the direction similarity of rivers + buildings and roads + buildings changed little. View Full-Text
Keywords: geometric similarity; micro scene; cartographic generalization; direction Voronoi diagram; position graph; quality assessment; Hausdorff distance; curvature geometric similarity; micro scene; cartographic generalization; direction Voronoi diagram; position graph; quality assessment; Hausdorff distance; curvature
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MDPI and ACS Style

Yang, F.; Wang, Z.; Yan, H.; Lu, X. Geometric Similarity Measurement Method for Micro Scene Generalization. Appl. Sci. 2022, 12, 628. https://doi.org/10.3390/app12020628

AMA Style

Yang F, Wang Z, Yan H, Lu X. Geometric Similarity Measurement Method for Micro Scene Generalization. Applied Sciences. 2022; 12(2):628. https://doi.org/10.3390/app12020628

Chicago/Turabian Style

Yang, Fei, Zhonghui Wang, Haowen Yan, and Xiaomin Lu. 2022. "Geometric Similarity Measurement Method for Micro Scene Generalization" Applied Sciences 12, no. 2: 628. https://doi.org/10.3390/app12020628

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