Coastline Matching Process Based on the Discrete Fréchet Distance

Mascret, Ariane; Devogèle, Thomas; Berre, Iwan Le; Hénaff, Alain

doi:10.1007/3-540-35589-8_25

Cited by 25 publications

(23 citation statements)

References 7 publications

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“…Considering the nature of flight trajectories, the discrete Fréchet distance as defined in Eiter and Mannila (), which is a computationally efficient approximation to the Fréchet distance, is used in the present study. Following the methodology described in Mascret et al (), for a given pair of trajectories L 1 and L 2 with n and m waypoints, respectively, a Fréchet matrix M F of size n × m is computed with its element defined as follows:

M_{F} (() i, j) = \max ((), \begin{matrix} center & d_{E} ((), L_{1} ((), i), L_{2} ((), j)) \\ center & \min ((), \begin{matrix} center & M_{F} ((), i - 1, j) \\ center & M_{F} ((), i, j - 1) \\ center & M_{F} ((), i - 1, j - 1) \end{matrix}) \end{matrix})

where 1 ≤ i ≤ n , 1 ≤ j ≤ m and d E ( L 1 ( i ), L 2 ( j )) denotes the Euclidean distance between the i th waypoint on L 1 and j th waypoint on L 2 . After iterating through all the possible combinations of i and j , the discrete Fréchet distance d Fd between L 1 and L 2 is given by the last element in M F , that is, M F ( n , m ).…”

Section: Methodsmentioning

confidence: 99%

Quantification of spatial spread in track‐based applications

Cheung

2016

Meteorological Applications

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Traditionally, spaghetti charts and track ellipses are used in track-based meteorological applications to illustrate the spatial uncertainty of a set of paths on a map. However, the spatial spread of the set is often evaluated by the eye, and as a result, the decisions made can be highly subjective. A unit-less metric based on the Fréchet distance is developed and proposed as a consistent way of quantifying spatial uncertainty in track-based applications. The methodology described is generic and can be configured for use in applications where a measure of the spatial similarity among a set of tracks is required, such as tropical storm track forecasts and flight/ship routing.

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M_{F} (() i, j) = \max ((), \begin{matrix} center & d_{E} ((), L_{1} ((), i), L_{2} ((), j)) \\ center & \min ((), \begin{matrix} center & M_{F} ((), i - 1, j) \\ center & M_{F} ((), i, j - 1) \\ center & M_{F} ((), i - 1, j - 1) \end{matrix}) \end{matrix})

Section: Methodsmentioning

confidence: 99%

Quantification of spatial spread in track‐based applications

Cheung

2016

Meteorological Applications

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“…In the matching of the same type patterns (event 1), the similarity can be measured by distances like nearest distance, Hausdorff distance, and Fréchet distance (Alt and Godau 1995;Mascret et al 2006). In the matching of the same type patterns (event 1), the similarity can be measured by distances like nearest distance, Hausdorff distance, and Fréchet distance (Alt and Godau 1995;Mascret et al 2006).…”

Section: Methods For Automatic Matching Processmentioning

confidence: 99%

Advances in Spatial Data Handling and GIS

Shi

Yeh²,

Leung³

et al. 2012

Lecture Notes in Geoinformation and Cartography

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“…Other distances between lines may be defined to go further. Measures based on the Fréchet distance are for example particularly pertinent to compare very sinuous lines like coastlines [1], [8], [15], but detailing these measures is out of the scope of this paper.…”

Section: Comparing Geometries Of Nodes and Arcsmentioning

confidence: 99%

Matching Networks with Different Levels of Detail

2007

Self Cite

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This paper deals with the issue of automatically matching networks with different levels of details. We first present why this issue is complex through an analysis of the differences that can be encountered between networks. We also present different criteria, tools and approaches used for network matching. We then propose a matching process, named NetMatcher. This process is a several steps process looking for potential candidates and then analysing them in order to determine the final results. It relies on the comparison of geometrical, attributive, and topological properties of objects. It determines one-to-many links between networks: in particular a node of the less detailed network can be matched to several arcs and nodes forming a complex junction in the most detailed network. An important strength of the process is to self-evaluate its results through the comparison of topological organisation of networks. This paves the way to an interactive editing of the results. The NetMatcher process has been intensively tested on a wide range of actual datasets, thus highlighting its effectiveness as well as its limits.

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Coastline Matching Process Based on the Discrete Fréchet Distance

Cited by 25 publications

References 7 publications

Quantification of spatial spread in track‐based applications

Quantification of spatial spread in track‐based applications

Advances in Spatial Data Handling and GIS

Matching Networks with Different Levels of Detail

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