Abstract
Road-matching processes establish links between multi-sourced road lines representing the same entities in the real world. Several road-matching methods have been developed in the last three decades. The main issue related to this process is selecting the most appropriate method. This selection depends on the data and requires a pre-process (i.e., accuracy assessment). This paper presents a new matching method for roads composed of different patterns. The proposed method matches road lines incrementally (i.e., from the most similar matching to the least similar). In the experimental testing, three road networks in Istanbul, Turkey, which are composed of tree, cellular, and hybrid patterns, provided by the municipality (authority), OpenStreetMap (volunteered), TomTom (private), and Basarsoft (private) were used. The similarity scores were determined using Hausdorff distance, orientation, sinuosity, mean perpendicular distance, mean length of triangle edges, and modified degree of connectivity. While the first four stages determined certain matches with regards to the scores, the last stage determined them with a criterion for overlapping areas among the buffers of the candidates. The results were evaluated with manual matching. According to the precision, recall, and F-value, the proposed method gives satisfactory results on different types of road patterns.
Highlights
Map conflation has been a popular research field in geographical information science since the first studies on roads in the 1980s by Lynch and Saalfeld [1], Rosen and Saalfeld [2], Lupien and Moreland [3], and Saalfeld [4]
A new matching process has been conducted with large datasets
The statistical result of the matching study was computed by comparing the road lines of samples in Figure 14 to manual matchings
Summary
Map conflation has been a popular research field in geographical information science since the first studies on roads in the 1980s by Lynch and Saalfeld [1], Rosen and Saalfeld [2], Lupien and Moreland [3], and Saalfeld [4]. The volume of spatial data is rapidly increasing by means of several sources, including governmental and private agencies, and volunteers. Some of these sources are based on sensors of remote sensing (light detection and ranging (LiDAR), unmanned aerial vehicle (UAV) imagery, internet of things (IoT) sensors). Using these kinds of spatial data requires an integration process that handles geo-information fusion. Map conflation definition agrees with the traditional definition of data fusion that is commonly used in computer science and remote sensing fields [6]
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