Template Matching and Simplification Method for Building Features Based on Shape Cognition

Yan, Xiongfeng; Ai, Tinghua; Zhang, Xiang

doi:10.3390/ijgi6080250

Cited by 34 publications

(18 citation statements)

References 32 publications

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“…To evaluate the feasibility and adaptability of our method, the change in the number of vertices (NumC), the change in the area (AreaC), the change in ratio of orthogonal vertices (OrtC), the change in position of the center point (CtrPC), the change in shape in global (SGC), and the change in shape in details (SDC) [8,41] are selected as the evaluation indicators, which are defined as the following:…”

Section: Determination Of the Simplification Evaluation Indicatorsmentioning

confidence: 99%

“…In template-based methods, the building is simplified by replacing with predefined templates, which are similar to the building with a simpler form. Rainsford and Mackaness (2002) [4] proposed a building simplification method based on shape matching, which preserves the shape characteristics and area after simplification with strong practicability [38][39][40][41]. However, due to the limitation of the template library type, it is not ideal for polygon simplification with complex structures.…”

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confidence: 99%

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A Progressive Simplification Method for Buildings Based on Structural Subdivision

Zhai

Yin

et al. 2022

IJGI

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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 (1:1K) 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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Section: Determination Of the Simplification Evaluation Indicatorsmentioning

confidence: 99%

mentioning

confidence: 99%

A Progressive Simplification Method for Buildings Based on Structural Subdivision

Zhai

Yin

et al. 2022

IJGI

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“…It consists of a total of 5010 buildings. According to their shapes, each building was labeled with a similar letter in the alphabet as shown in Figure 3 [34]. Finally, there were 10 different shape types in the dataset, such as O-shape, Y-shape, and F-shape.…”

Section: Experimental Dataset and Preprocessingmentioning

confidence: 99%

TriangleConv: A Deep Point Convolutional Network for Recognizing Building Shapes in Map Space

Yao-hui

Zheng

et al. 2021

IJGI

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The classification and recognition of the shapes of buildings in map space play an important role in spatial cognition, cartographic generalization, and map updating. As buildings in map space are often represented as the vector data, research was conducted to learn the feature representations of the buildings and recognize their shapes based on graph neural networks. Due to the principles of graph neural networks, it is necessary to construct a graph to represent the adjacency relationships between the points (i.e., the vertices of the polygons shaping the buildings), and extract a list of geometric features for each point. This paper proposes a deep point convolutional network to recognize building shapes, which executes the convolution directly on the points of the buildings without constructing the graphs and extracting the geometric features of the points. A new convolution operator named TriangleConv was designed to learn the feature representations of each point by aggregating the features of the point and the local triangle constructed by the point and its two adjacency points. The proposed method was evaluated and compared with related methods based on a dataset consisting of 5010 vector buildings. In terms of accuracy, macro-precision, macro-recall, and macro-F1, the results show that the proposed method has comparable performance with typical graph neural networks of GCN, GAT, and GraphSAGE, and point cloud neural networks of PointNet, PointNet++, and DGCNN in the task of recognizing and classifying building shapes in map space.

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“…The distance d A,B (t,θ) is the turning function similarity between A and B. For more details, please refer to previously published reports [26,27]. This shape similarity measure is used in here in two ways: to measure the general similarity of shapes, and to address the issue where very similar shapes are not aligned with each other.…”

Section: Global Shape Similarity Using Turning Functionmentioning

confidence: 99%

Change Detection for Building Footprints with Different Levels of Detail Using Combined Shape and Pattern Analysis

Zhou

Chen

Zhang

et al. 2018

IJGI

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Crowd-sourced geographic information is becoming increasingly available, providing diverse and timely sources for updating existing spatial databases to facilitate urban studies, geoinformatics, and real estate practices. However, the discrepancies between heterogeneous datasets present challenges for automated change detection. In this paper, we identify important measurable factors to account for issues like boundary mismatch, large offset, and discrepancies in the levels of detail between the more current and to-be-updated datasets. These factors are organized into rule sets that include data matching, merge of the many-to-many correspondence, controlled displacement, shape similarity, morphology of difference parts, and the building pattern constraint. We tested our approach against OpenStreetMap and a Dutch topographic dataset (TOP10NL). By removing or adding some components, the results show that our approach (accuracy = 0.90) significantly outperformed a basic geometric method (0.77), commonly used in previous studies, implying a more reliable change detection in realistic update scenarios. We further found that distinguishing between small and large buildings was a useful heuristic in creating the rules.

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Template Matching and Simplification Method for Building Features Based on Shape Cognition

Cited by 34 publications

References 32 publications

A Progressive Simplification Method for Buildings Based on Structural Subdivision

A Progressive Simplification Method for Buildings Based on Structural Subdivision

TriangleConv: A Deep Point Convolutional Network for Recognizing Building Shapes in Map Space

Change Detection for Building Footprints with Different Levels of Detail Using Combined Shape and Pattern Analysis

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