Harmonic generalization based on the integrated geographic feature retrieval

Lina Huang; Lifan Fei; Jing He

doi:10.1117/12.838301

14 October 2009 Harmonic generalization based on the integrated geographic feature retrieval

Lina Huang, Lifan Fei, Jing He

Proceedings Volume 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining; 74921H (2009) https://doi.org/10.1117/12.838301
Event: International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, 2009, Wuhan, China

Abstract

Generalization is needed to describe relevant information on an appropriate level of detail. However, the harmony between different generalized geographic features are always difficult to be ensured even if the data sources come from the same topographic maps or geographic databases, as the generalization is carried out separately in the context of computer assisted cartography. This paper introduces a new approach for the harmonic generalization of terrain and water system based on the integrated geographic feature retrieval using 3D Douglas-Peucker algorithm. The advantage of the research is two folded: firstly, it focuses on the geographic nature of water system and terrain; secondly, the 3D Douglas-Peucker algorithm is developed to make this generalization of the two kinds of features possible. The spatial representation of water system in vector data and that of the terrain in DEMs are unitized into one set of general character points. Then the 3D Douglas-Peucker algorithm is performed for the features retrieval. After that, the result is returned to generate the abstracted terrain and the simplified water system. In this way, the harmonic registration between the generalized terrain and the generalized water system can be ensured. The preliminary experiments show that this harmonic generalization is a promising way both in cartography and GIS.

Citation Download Citation

Lina Huang, Lifan Fei, and Jing He "Harmonic generalization based on the integrated geographic feature retrieval", Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74921H (14 October 2009); https://doi.org/10.1117/12.838301

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