A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm

Chehreghan, Alireza; Abbaspour, Rahim Ali

doi:10.1080/15230406.2017.1324823

Cited by 30 publications

(21 citation statements)

References 48 publications

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“…For linear matching, most studies have compared multiple similarities (e.g., geometry, semantic, topology) and applied node-based, arc-based, polygon-based, or hybrid matching strategies [19][20][21][22][23]. Recent advances have introduced relaxation labelling, logistic regression, and genetic algorithms to improve the performance of road network matching [24][25][26]. Du et al [27] designed ontology descriptions and fusion operators to integrate authoritative and crowdsourced road data.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Matching of Multi-Scale Building Data Based on Relaxation Labelling and Pattern Combinations

Zhang

Huang

Deng

et al. 2019

IJGI

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With the increasingly urgent demand for map conflation and timely data updating, data matching has become a crucial issue in big data and the GIS community. However, non-rigid deviation, shape homogenization, and uncertain scale differences occur in crowdsourced and official building data, causing challenges in conflating heterogeneous building datasets from different sources and scales. This paper thus proposes an automated building data matching method based on relaxation labelling and pattern combinations. The proposed method first detects all possible matching objects and pattern combinations to create a matching table, and calculates four geo-similarities for each candidate-matching pair to initialize a probabilistic matching matrix. After that, the contextual information of neighboring candidate-matching pairs is explored to heuristically amend the geo-similarity-based matching matrix for achieving a contextual matching consistency. Three case studies are conducted to illustrate that the proposed method obtains high matching accuracies and correctly identifies various 1:1, 1:M, and M:N matching. This indicates the pattern-level relaxation labelling matching method can efficiently overcome the problems of shape homogeneity and non-rigid deviation, and meanwhile has weak sensitivity to uncertain scale differences, providing a functional solution for conflating crowdsourced and official building data.

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Section: Literature Reviewmentioning

confidence: 99%

Automated Matching of Multi-Scale Building Data Based on Relaxation Labelling and Pattern Combinations

Zhang

Huang

Deng

et al. 2019

IJGI

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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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“…Tourist interest should be considered to be the core factor in developing smart tourism recommendation systems, and it is also the principal condition for developing tour route planning algorithms. Secondly, providing tourists with optimal tourist sites and tour routes as well as tour decision support efficiently and in accordance with their needs and interests, is the aim of smart tourism development [7][8][9][10]. By setting up a tourist interest machine learning model based on tourism big data, individual needs and interests tendencies can be predicted and output, which is the front end for smart GIS or smart tourism recommendation systems.…”

Section: Introductionmentioning

confidence: 99%

Smart Tour Route Planning Algorithm Based on Naïve Bayes Interest Data Mining Machine Learning

Zhou

Liu

et al. 2020

IJGI

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A smart tour route planning algorithm based on a Naïve Bayes interest data mining machine learning is brought forward in the paper, according to the problems of current tour route planning methods. A machine learning model of Naïve Bayes interest data mining is set up by learning a mass of training data on tourists’ interests and needs. Through the recommended interest tourist site classifications from the machine learning module, the optimal tourist site mining algorithm based on the membership degree searching propagating tree of a tourist’s temporary accommodation is set up, which mines and outputs the optimal tourist sites. The mined optimal tourist sites are taken as seed points to set up a tour route planning algorithm based on the optimal propagating tree of a closed-loop structure. Through the proposed algorithm, an experiment is designed and performed to output optimal tour routes conforming to tourists’ needs and interests, including the propagating tree closed-loop structures, a minimum heap of propagating tree weight function value, and a weight function value complete binary tree. We prove that the proposed algorithm has the features of intelligence and accuracy, and it can learn tourists’ needs and interests to output optimal tourist sites and tour routes and ensure that tourists can get the best motive benefits and travel experience in the tour process, by analyzing the experiment data and results.

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A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm

Cited by 30 publications

References 48 publications

Automated Matching of Multi-Scale Building Data Based on Relaxation Labelling and Pattern Combinations

Automated Matching of Multi-Scale Building Data Based on Relaxation Labelling and Pattern Combinations

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

Smart Tour Route Planning Algorithm Based on Naïve Bayes Interest Data Mining Machine Learning

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