A Study on a Matching Algorithm for Urban Underground Pipelines

Abstract

Urban underground pipelines are known as "urban blood vessels". To detect changes in integrated pipelines and professional pipelines, the matching of same-name spatial objects is critical. Existing algorithms used for vector network matching were analyzed to develop an improved matching algorithm that can adapt to underground pipeline networks. Our algorithm improves the holistic matching of pipeline strokes, and also a partial matching algorithm is provided. In this study, appropriate geometric measures were selected to calculate the geometric similarity between pipeline strokes in their holistic matching. Existing methods for evaluating similarities in spatial scene structures in partial underground pipeline networks were improved. A method of partial matching of strokes was additionally investigated, and it compensates for the deficiencies of holistic stroke matching. Experiments showed that the matching performance was good, and the operation efficiency was high. geometric connection rules [5,12]. This principle was combined with the characteristics of road and water network spatial data to propose the stroke concept [12,13]. From a local indicator, two road segments can be connected on the condition that their deflection angle is less than a specified threshold. The deflection angle, which ranges from 0 • to 180 • represents the degree of deviation formed by two linked road segments [14], as shown in Figure 1. Figure 1a shows the pipeline spatial data organized in the traditional "node-segment" manner, and Figure 1b shows the data processed by stroke connection. There are four strokes: stroke1<a, b, c, d>, stroke2<e, f>, stroke3<g>, and stroke4<h>. "Good continuation" usually refers to an approximately level angle (180 • ) between segments. The geometric morphology of a stroke is characterized by a smooth and continuous distribution in space, which maintains the integrity of urban underground pipelines. Therefore, the stroke can be used as the basic unit for matching and analyzing urban underground pipelines.

Proposal of a method for evaluating the spatial distribution pattern of linear features

“…As a measure of spacing between lines, we chose to use the Hausdorff Distance metric. Hausdorff Distance is widely used for information retrieval and analysis of geometric similarity between vector objects (for points, lines or polygons) or images (Huttenlocher et al 1992;Ariza-López and Mozas-Calvache 2012;Chehreghan and Ali Abbaspour 2017;Marošević 2018;Wang et al 2019). According to Atkinson-Gordo and Ariza-López (2002), this distance is recurrently applied to the evaluation of positional quality (Mozas-Calvache and Ariza-López 2015; Santos et al 2015;Mozas-Calvache et al 2017a, 2017bSaito et al 2019) and in processes to control the effectiveness of cartographic generalization (Zhai et al 2017;Guo et al 2019;Liu et al 2020).…”

Combined Matching Approach of Road Networks Under Different Scales Considering Constraints of Cartographic Generalization

“…The geometric similarity is inadequate in multi-scale road network matching without the topological constraints [61]. In our algorithm, the structural similarity is calculated using the spatial scene of a stroke.…”

Multi-Scale Road Matching Based on the Summation Product of Orientation and Distance and Shape Descriptors

“…In terms of algorithmic improvements for matching data from multiple sources: Wu, J. et al [15] presented a general approach using the Voronoi diagram for spatial entity matching on multi-scale datasets. Wang, S. et al [16] analyzed existing algorithms used for vector network matching to develop an improved matching algorithm that can adapt to underground pipeline networks. Zhang, J. et al [17] proposed an improved probabilistic relaxation method, considering both local and global optimizations for the matching of multi-scale of road networks.…”