Ad hoc matching of vectorial road networks

In integration of road maps modeled as road vector data, the main task is matching pairs of objects that represent, in different maps, the same segment of a real-world road. In an ad hoc integration, the matching is done for a specific need and, thus, is performed in real time, where only a limited preprocessing is possible. Usually, ad hoc integration is performed as part of some interaction with a user and, hence, the matching algorithm is required to complete its task in time that is short enough for human users to provide feedback to the application, that is, in no more than a few seconds. Such interaction is typical of services on the World Wide Web and to applications in car-navigation systems or in handheld devices. Several algorithms were proposed in the past for matching road vector data; however, these algorithms are not efficient enough for ad hoc integration. This article presents algorithms for ad hoc integration of maps in which roads are represented as polylines. The main novelty of these algorithms is in using only the locations of the endpoints of the polylines rather than trying to match whole lines. The efficiency of the algorithms is shown both analytically and experimentally. In particular, these algorithms do not require the existence of a spatial index, and they are more efficient than an alternative approach based on using a grid index. Extensive experiments using various maps of three different cities show that our approach to matching road networks is efficient and accurate (i.e., it provides high recall and precision). General Terms:Algorithms, Experimentation

[1]  Craig A. Knoblock,et al.  Automatically Conflating Road Vector Data with Orthoimagery , 2006, GeoInformatica.

[2]  J. F. Hangouet COMPUTATION OF THE HAUSDORFF DISTANCE BETWEEN PLANE VECTOR POLYLINES , 2008 .

[3]  Thomas Devogele,et al.  Matching Networks with Different Levels of Detail , 2008, GeoInformatica.

[4]  Gio Wiederhold,et al.  Meditation to Deal with Heterogeneous Data Sources , 1999, INTEROP.

[5]  Jan-Henrik Haunert Link based Conflation of Geographic Datasets , 2005 .

[6]  Agnès Voisard,et al.  Spatial Databases: With Application to GIS , 2001 .

[7]  Yehoshua Sagiv,et al.  Integrating Data from Maps on the World-Wide Web , 2006, W2GIS.

[8]  Frederick E. Petry,et al.  A Rule-based Approach for the Conflation of Attributed Vector Data , 1998, GeoInformatica.

[9]  Zoé Lacroix,et al.  A WFS-based mediation system for GIS interoperability , 2002, GIS '02.

[10]  Stefano Spaccapietra,et al.  The MurMur project: Modeling and querying multi-representation spatio-temporal databases , 2006, Inf. Syst..

[11]  Edward M. Mikhail,et al.  Observations And Least Squares , 1983 .

[12]  S. Volz,et al.  LINKING DIFFERENT GEOSPATIAL DATABASES BY EXPLICIT RELATIONS , 2004 .

[13]  Val Noronha,et al.  Towards ITS Map Database Interoperability—Database Error and Rectification , 2000, GeoInformatica.

[14]  Alan Saalfeld,et al.  Conflation Automated map compilation , 1988, Int. J. Geogr. Inf. Sci..

[15]  Monika Sester,et al.  Linking Objects of Different Spatial Data Sets by Integration and Aggregation , 1998, GeoInformatica.

[16]  Catriel Beeri,et al.  Object Fusion in Geographic Information Systems , 2004, VLDB.

[17]  Christopher B. Jones,et al.  Matching and aligning features in overlayed coverages , 1998, GIS '98.

[18]  Demin Xiong,et al.  Semiautomated matching for network database integration , 2004 .

[19]  Frederico T. Fonseca,et al.  Ontology-driven geographic information systems , 1999, GIS '99.

[20]  Wenzhong Shi,et al.  A probability-based multi-measure feature matching method in map conflation , 2009 .

[21]  K. Yétongnon,et al.  GIS INTEROPERABILITY, FROM PROBLEMS TO SOLUTIONS , 2011 .

[22]  Stefano Spaccapietra,et al.  On Spatial Database Integration , 1998, Int. J. Geogr. Inf. Sci..

[23]  Frederico T. Fonseca,et al.  Using Ontologies for Integrated Geographic Information Systems , 2002, Trans. GIS.

[24]  Volker Walter,et al.  Matching spatial data sets: a statistical approach , 1999, Int. J. Geogr. Inf. Sci..

[25]  M. Goodchild,et al.  AUTOMATICALLY AND ACCURATELY MATCHING OBJECTS IN GEOSPATIAL DATASETS , 2011 .

[26]  K. Lowell,et al.  Spatial Accuracy Assessment : Land Information Uncertainty in Natural Resources , 1999 .

[27]  Catriel Beeri,et al.  Finding corresponding objects when integrating several geo-spatial datasets , 2005, GIS '05.

[28]  Martien Molenaar,et al.  Ontology-Based Geographic Data Set Integration , 1999, Spatio-Temporal Database Management.

[29]  S. Filin,et al.  Transformation of Datasets in a Linear-based Map Conflation Framework , 2001 .

[30]  Gio Wiederhold,et al.  Mediators in the architecture of future information systems , 1992, Computer.

[31]  Michela Bertolotto,et al.  Progressive Transmission of Vector Map Data over the World Wide Web , 2001, GeoInformatica.

[32]  Catriel Beeri,et al.  Location‐based algorithms for finding sets of corresponding objects over several geo‐spatial data sets , 2010, Int. J. Geogr. Inf. Sci..

[33]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.