A new descriptor for improving geometric-based matching of linear objects on multi-scale datasets

Chehreghan, Alireza; Abbaspour, Rahim Ali

doi:10.1080/15481603.2017.1338390

Cited by 11 publications

(10 citation statements)

References 38 publications

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“…Object matching refers to the identification of the objects with an equivalent entity in several datasets [58][59][60][61]. The main aim of object matching was to identify the corresponding objects across historical versions in the OSM history file.…”

Section: Object Matchingmentioning

confidence: 99%

Improving the Quality of Citizen Contributed Geodata through Their Historical Contributions: The Case of the Road Network in OpenStreetMap

Nasiri

Abbaspour

Chehreghan

et al. 2018

IJGI

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OpenStreetMap (OSM) has proven to serve as a promising free global encyclopedia of maps with an increasing popularity across different user communities and research bodies. One of the unique characteristics of OSM has been the availability of the full history of users' contributions, which can leverage our quality control mechanisms through exploiting the history of contributions. Since this aspect of contributions (i.e., historical contributions) has been neglected in the literature, this study aims at presenting a novel approach for improving the positional accuracy and completeness of the OSM road network. To do so, we present a five-stage approach based on a Voronoi diagram that leads to improving the positional accuracy and completeness of the OSM road network. In the first stage, the OSM data history file is retrieved and in the second stage, the corresponding data elements for each object in the historical versions are identified. In the third stage, data cleaning on the historical datasets is carried out in order to identify outliers and remove them accordingly. In the fourth stage, through applying the Voronoi diagram method, one representative version for each set of historical versions is extracted. In the final stage, through examining the spatial relations for each object in the history file, the topology of the target object is enhanced. As per validation, a comparison between the latest version of the OSM data and the result of our approach against a reference dataset is carried out. Given a case study in Tehran, our findings reveal that the completeness and positional precision of OSM features can be improved up to 14%. Our conclusions draw attention to the exploitation of the historical archive of the contributions in OSM as an intrinsic quality indicator.

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Section: Object Matchingmentioning

confidence: 99%

Improving the Quality of Citizen Contributed Geodata through Their Historical Contributions: The Case of the Road Network in OpenStreetMap

Nasiri

Abbaspour

Chehreghan

et al. 2018

IJGI

Self Cite

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“…The basic framework for the assessment of OSM geometric quality is based on the comparison of OSM geometric data with authoritative geospatial data sources. Different approaches have been proposed to investigate the completeness and positional accuracy of road networks and buildings in OSM (Brovelli & Zamboni, 2018; Chehreghan & Abbaspour, 2017; Fairbairn & Al‐Bakri, 2013; Fan et al, 2014; Koukoletsos et al, 2012). Geoscientists have studied the OSM data in different countries such as Germany (Brückner et al, 2021; Hecht et al, 2013; Neis et al, 2012), France (Girres & Touya, 2010), UK (Haklay, 2010), Sweden (Abdolmajidi et al, 2015), Canada (Zhang, 2017), Iran (Forghani & Delavar, 2014; Minaei, 2020), and Turkey (Zia et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Semantic integration of OpenStreetMap and CityGML with formal concept analysis

Ahmadian

Pahlavani

2022

Transactions in GIS

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Volunteered geographic information (VGI) provides geometric and descriptive sources of geospatial data. VGI exchange, reuse, and integration are serious challenges due to the subjective contribution process, lack of organization, and redundancy. This study aims to enhance the quality of VGI semantic data by presenting a new approach to integrating and formalizing the VGI semantic knowledge using formal concept analysis. The proposed approach is assessed using the building tags in OpenStreetMap (OSM) and CityGML.The alignment process discovers the conceptual overlap between the categories of Amenity (Others), Office, and Man-Made in Map Features (OSM) and Business and Trade, Recreation, Sport, and Industry in AbstractBuilding (CityGML). The k-means clustering of the results illustrated that class, usage/function, address, wheelchair, and website/wikidata/wikipedia are significant attributes to describe building categories. Moreover, results showed that the analysis of frequent itemsets and cluster characteristics provides significant information about custom tags in OSM's editing tools.

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“…Li, Li, and Xie (2017) used the TF distance to evaluate the difference between different generalizations of a building's footprint that they produced using a “morphing” algorithm. The possibility of using the TF distance to measure polyline similarity was also discussed by Zhang (2009) and Chehreghan and Abbaspour (2017a), but no experimental results are reported.…”

Section: Introductionmentioning

confidence: 99%

“…The area is computed as the accumulated

L_{p}

distance of the TF over the unit length, with

L_{2}

distance being used originally (Arkin et al., 1991). Zhang (2009) and Chehreghan and Abbaspour (2017a) described the formula of the shape dissimilarity measure between polylines A and B (using

L_{1}

distance) as shown in Equation (1):

\begin{matrix} T F (A, B) = {false\int}_{0}^{1} f (θ_{A} (t), θ_{B} (t)) d t \\ f (θ_{A}, θ_{B}) = \{\begin{matrix} |θ_{A} ‐ θ_{B}|, if |θ_{A} ‐ θ_{B}| \leq π \\ 2 π ‐ (||, θ_{A} ‐ θ_{B}), otherwise \end{matrix}) \end{matrix}

…”

Section: Introductionmentioning

confidence: 99%

Conflating linear features using turning function distance: A new orientation‐sensitive similarity measure

Lei

Wang

2021

Transactions in GIS

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Measuring the similarity between counterpart geospatial features is crucial in the effective conflation of spatial datasets from difference sources. This article proposes a new similarity metric called the “map turning function distance” (MTFD) for matching linear features such as roads based on the well‐known turning function (TF) distance in computer vision. The MTFD overcomes the limitations of the traditional TF distance, such as the inability to handle partial matches and insensitivity to differences in scale and rotation. In particular, the MTFD allows one to: (a) partially match a linear feature to a portion of a larger feature from a certain position of match; and (b) consider both the shape and orientation differences of polylines based on comparing their turning angles. In finding the best match position, we prove that the optimal position can be found among a finite set of positions on the target feature. We then combine the MTFD with widely used point‐offset distances such as the Hausdorff distance to form a composite similarity metric. Our experiments with real road datasets demonstrate that the new metric has greater discriminative power than traditional point‐offset‐based similarity measures, and significantly improves the precision of two tested conflation models.

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A new descriptor for improving geometric-based matching of linear objects on multi-scale datasets

Cited by 11 publications

References 38 publications

Improving the Quality of Citizen Contributed Geodata through Their Historical Contributions: The Case of the Road Network in OpenStreetMap

Improving the Quality of Citizen Contributed Geodata through Their Historical Contributions: The Case of the Road Network in OpenStreetMap

Semantic integration of OpenStreetMap and CityGML with formal concept analysis

Conflating linear features using turning function distance: A new orientation‐sensitive similarity measure

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