A Progressive Simplification Method for Buildings Based on Structural Subdivision

Renjian Zhai · Anping Li · Jichong Yin · Jiawei Du · Yue Qiu

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Geographic Information Systems Studies 3D Modeling in Geospatial Applications Automated Road and Building Extraction

Abstract

Building simplification is an important research area in automatic map generalization. Up to now, many approaches have been proposed by scholars. However, in the continuous transformation of scales for buildings, keeping the main shape characteristics, area, and orthogonality of buildings are always the key and difficult points. Therefore, this paper proposes a method of progressive simplification for buildings based on structural subdivision. In this paper, iterative simplification is adopted, which transforms the problem of building simplification into the simplification of the minimum details of building outlines. Firstly, a top priority structure (TPS) is determined, which represents the smallest detail in the outline of the building. Then, according to the orthogonality and concave–convex characteristics, the TPS are classified as 62 subdivisions, which cover the local structure of the building polygon. Then, the subdivisions are divided into four simplification types. The building is simplified to eliminate the TPS continuously, retaining the right-angle characteristics and area as much as possible, until the results satisfy the constraints and rules of simplification. A topographic dataset (1:1 K) collected from Kadaster was used for our experiments. In order to evaluate the algorithm, many tests were undertaken, including tests of multi-scale simplification and simplification of typical buildings, which indicate that this method can realize multi-scale presentation of buildings. Compared with the existing simplification methods, the comparison results show that the proposed method can simplify buildings effectively, which has certain advantages in keeping shape characteristics, area, and rectangularity.

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References

2005Optimization approaches for generalization and data abstraction 1942Map Makers Are Human: Comments on the Subjective in Maps 2019Learning Cartographic Building Generalization with Deep Convolutional Neural Networks 2018BUILDING GENERALIZATION USING DEEP LEARNING 2017Template Matching and Simplification Method for Building Features Based on Shape Cognition 2019A polygon aggregation method with global feature preservation using superpixel segmentation 2018A new approach to simplifying polygonal and linear features using superpixel segmentation 2009Automated Building Simplification Using a Recursive Approach

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