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mj-msc.tex
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mj-msc.tex
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\newcommand{\WM}{Wang--M{\"u}ller}
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\newcommand{\WM}{Wang--M{\"u}ller}
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\title{
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\title{
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\includegraphics[width=60mm]{vu.png}\\[8ex]
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Cartographic Generalization of Lines using free software \\
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Cartographic Generalization of Lines using free software \\
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(example of rivers) \\ \vspace{4mm}
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(example of rivers) \\ \vspace{4mm}
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}
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}
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\iffalse
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\fi
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\author{Motiejus Jakštys}
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\author{Motiejus Jakštys}
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\date{
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\date{
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\vspace{10mm}
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\VCDescribe
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Version: \VCDescribe
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}
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}
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\begin{document}
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\begin{document}
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@ -55,7 +52,7 @@
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Current open-source line generalization solutions have their roots in
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Current open-source line generalization solutions have their roots in
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mathematics and geometry, and are not fit for natural objects like rivers
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mathematics and geometry, and are not fit for natural objects like rivers
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and coastlines. This paper discusses our implementation of \WM algorithm
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and coastlines. This paper discusses our implementation of {\WM} algorithm
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under and open-source license, explains things that we would had
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under and open-source license, explains things that we would had
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appreciated in the original paper and compares our results to different
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appreciated in the original paper and compares our results to different
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generalization algorithms.
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generalization algorithms.
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@ -71,16 +68,70 @@ Current open-source line generalization solutions have their roots in
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\section{Introduction}
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\section{Introduction}
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\label{sec:introduction}
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\label{sec:introduction}
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A number of cartographic line generalization algorithms have been researched,
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When creating small-scale maps, often the detail of the data source is greater
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which claim to better process cartographic objects like lines. These fall into
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than desired for the map. This becomes especially acute for natural features
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two rough categories:
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that have many bends, like coastlines, rivers and forest boundaries.
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To create a small-scale map from a large-scale data source, these features need
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to be generalized: detail should be reduced. However, while doing so, it is
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important to preserve the "defining" shape of the original feature, otherwise
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the result will look unrealistic.
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For example, if a river is nearly straight, it should be nearly straight after
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generalization, otherwise a too straightened river will look like a canal.
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Conversely, if the river is highly wiggly, the number of bends should be
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reduced, but not removed.
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Generalization problem for other objects can often be solved by other
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non-geometric means:
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\begin{itemize}
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\item Towns and cities can be filtered and generalized by number of
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inhabitants.
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\item Roads can be eliminated by the road length, number of lanes, or
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classification of the road (local, regional, international).
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\end{itemize}
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Natural line generalization problem can be viewed as having two competing
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goals:
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\begin{itemize}
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\item Reduce detail by removing or simplifying "less important" features.
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\item Retain enough detail, so the original is still recognize-able.
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\end{itemize}
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Given the discussed complexities, a fine line between under-generalization
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(leaving object as-is) and over-generalization (making a straight line) must be
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found. Therein lies the complexity of generalization algorithms: all have
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different trade-offs.
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\section{Literature review}
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\label{sec:literature-review}
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A number of cartographic line generalization algorithms have been researched.
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The "classical" ones are {\DP} and {\VW}.
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\subsection{{\DP} and {\VW}}
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\cite{douglas1973algorithms} and \cite{visvalingam1993line} are "classical"
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line generalization computer graphics algorithms. They are relatively simple to
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implement, require few runtime resources. Both of them accept only a single
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parameter, which makes them very simple to adjust for different scales.
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However, both of them are emitting insufficient
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\subsection{Modern approaches}
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After {\DP} and {\VW} have been established,
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These fall into two rough categories:
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\begin{itemize}
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\begin{itemize}
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\item Cartographic knowledge was encoded to an algorithm (bottom-up
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\item Cartographic knowledge was encoded to an algorithm (bottom-up
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approach). One among these are \cite{wang1998line}.
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approach). One among these are \cite{wang1998line}.
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\item Mathematical shape transformation which yields a more
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\item Mathematical shape transformation which yields a more cartographic
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cartographically suitable down-scaling. E.g. \cite{jiang2003line},
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result. E.g. \cite{jiang2003line}, \cite{dyken2009simultaneous},
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\cite{dyken2009simultaneous}, \cite{mustafa2006dynamic},
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\cite{mustafa2006dynamic}, \cite{nollenburg2008morphing}.
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\cite{nollenburg2008morphing}.
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\end{itemize}
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\end{itemize}
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During research for the mentioned articles, prototype code has been written for
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During research for the mentioned articles, prototype code has been written for
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@ -113,10 +164,8 @@ those through a widely available \cite{chaikin1974algorithm} smoothing
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algorithm via \href{https://postgis.net/docs/ST_ChaikinSmoothing.html}{PostGIS
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algorithm via \href{https://postgis.net/docs/ST_ChaikinSmoothing.html}{PostGIS
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ChaikinSmoothing}.
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ChaikinSmoothing}.
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\section{Visual comparison}
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\section{Methodology}
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\label{sec:methodology}
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\subsection{Comparison algorithms and parameters}
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\subsection{Combining bends}
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\section{Conclusions}
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\section{Conclusions}
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\label{sec:conclusions}
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\label{sec:conclusions}
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