Variable-Scale Visualization of High-Density Polygonal Buildings on a Tile Map

To better satisfy user’s needs for the accurate visualization of massive amounts of geographic data, the variable-scale expression of map content based on multilevel data organization has attracted increasing attention. Traditional methods based on vector data usually cannot handle tile data in the...

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Bibliographic Details
Published in:ISPRS international journal of geo-information 2022-10, Vol.11 (10), p.505
Main Authors: Chen, Zhixiong, Shen, Yilang, Lv, Xinlin, Qin, Qiaolin, Chen, Xin
Format: Article
Language:English
Subjects:
Algorithms
architectural typification extraction
Buildings
Clustering
Clutter
Data sources
Density
Design techniques
Digital mapping
hierarchical data organization
high-density buildings
LSC superpixel segmentation
Methods
Scale models
Segmentation
Spatial distribution
Unstructured data
variable visualization
variable-scale map
Visual effects
Visualization
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Summary:To better satisfy user’s needs for the accurate visualization of massive amounts of geographic data, the variable-scale expression of map content based on multilevel data organization has attracted increasing attention. Traditional methods based on vector data usually cannot handle tile data in the form of a grid on the network. Therefore, this paper proposes a variable-scale visualization method for high-density buildings based on a raster tile map. First, the buildings on a tile map are typified on the basis of linear spectral clustering (LSC) superpixel segmentation to reduce the number of buildings. Then, the shapes of buildings are simplified using the minimum bounding rectangle method. Lastly, the designed focus + glue + context (F + G + C) variable-scale model is used for visual output. The OpenStreetMap tile data are used to perform experiments. Compared with traditional methods, the proposed variable-scale visualization method in this paper considers the spatial distribution, quantity, and shape characteristics of buildings, reduces the clutter of data, and has a better (average value of building quantity, area and density is 57%) visual effect. Variable-scale visualization can be applied to unstructured map data sources and extended to grid data sources to improve the readability and recognizability of high-density buildings.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi11100505