Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 223-228, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-223-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 223-228, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-223-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

A MODEL STUDY OF SMALL-SCALE WORLD MAP GENERALIZATION

Y. Cheng1, Y. Yin1, C. M. Li1, W. Wu1, P. P. Guo1, X. L. Ma1, and F. M. Hu2 Y. Cheng et al.
  • 1Institute of Cartography and Geographic Information System, Chinese Academy of Surveying and Mapping, Beijing, China
  • 2School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China

Keywords: Global map, Map generalization, Improved model, Cross-platform symbol, Automatic map-making knowledge engine

Abstract. With the globalization and rapid development every filed is taking an increasing interest in physical geography and human economics. There is a surging demand for small scale world map in large formats all over the world. Further study of automated mapping technology, especially the realization of small scale production on a large scale global map, is the key of the cartographic field need to solve. In light of this, this paper adopts the improved model (with the map and data separated) in the field of the mapmaking generalization, which can separate geographic data from mapping data from maps, mainly including cross-platform symbols and automatic map-making knowledge engine. With respect to the cross-platform symbol library, the symbol and the physical symbol in the geographic information are configured at all scale levels. With respect to automatic map-making knowledge engine consists 97 types, 1086 subtypes, 21845 basic algorithm and over 2500 relevant functional modules.In order to evaluate the accuracy and visual effect of our model towards topographic maps and thematic maps, we take the world map generalization in small scale as an example. After mapping generalization process, combining and simplifying the scattered islands make the map more explicit at 1 : 2.1 billion scale, and the map features more complete and accurate. Not only it enhance the map generalization of various scales significantly, but achieve the integration among map-makings of various scales, suggesting that this model provide a reference in cartographic generalization for various scales.