TY - GEN

T1 - Noisy road network matching

AU - Diez, Yago

AU - Lopez, Mario A.

AU - Sellarès, J. Antoni

PY - 2008

Y1 - 2008

N2 - Let and be two road networks represented in vector form and covering rectangular areas R and R′, respectively, not necessarily parallel to each other, but with R′ ∈ R. We assume that and use different coordinate systems at (possibly) different, but known scales. Let and denote sets of "prominent" road points (e.g., intersections) associated with and , respectively. The positions of road points on both sets may contain a certain amount of "noise" due to errors and the finite precision of measurements. We propose an algorithm for determining approximate matches, in terms of the bottleneck distance, between and a subset of . We consider the characteristics of the problem in order to achieve a high degree of efficiency. At the same time, so as not to compromise the usability of the algorithm, we keep the complexity required for the data as low as possible. As the algorithm that guarantees to find a possible match is expensive due to the inherent complexity of the problem, we propose a lossless filtering algorithm that yields a collection of candidate regions that contain a subset S of such that may match a subset of S. Then we find possible approximate matchings between and subsets of S using the matching algorithm. We have implemented the proposed algorithm and report results that show the efficiency of our approach.

AB - Let and be two road networks represented in vector form and covering rectangular areas R and R′, respectively, not necessarily parallel to each other, but with R′ ∈ R. We assume that and use different coordinate systems at (possibly) different, but known scales. Let and denote sets of "prominent" road points (e.g., intersections) associated with and , respectively. The positions of road points on both sets may contain a certain amount of "noise" due to errors and the finite precision of measurements. We propose an algorithm for determining approximate matches, in terms of the bottleneck distance, between and a subset of . We consider the characteristics of the problem in order to achieve a high degree of efficiency. At the same time, so as not to compromise the usability of the algorithm, we keep the complexity required for the data as low as possible. As the algorithm that guarantees to find a possible match is expensive due to the inherent complexity of the problem, we propose a lossless filtering algorithm that yields a collection of candidate regions that contain a subset S of such that may match a subset of S. Then we find possible approximate matchings between and subsets of S using the matching algorithm. We have implemented the proposed algorithm and report results that show the efficiency of our approach.

UR - http://www.scopus.com/inward/record.url?scp=56749105640&partnerID=8YFLogxK

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U2 - 10.1007/978-3-540-87473-7_3

DO - 10.1007/978-3-540-87473-7_3

M3 - Conference contribution

AN - SCOPUS:56749105640

SN - 3540874720

SN - 9783540874720

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 38

EP - 54

BT - Geographic Information Science - 5th International Conference, GIScience 2008, Proceedings

PB - Springer Verlag

T2 - 5th International Conference on Geographic Information Science, GIScience 2008

Y2 - 23 September 2008 through 26 September 2008

ER -