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Motiejus Jakštys 2021-04-01 09:42:08 +03:00
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\newcommand{\WM}{Wang--M{\"u}ller}
\title{
\includegraphics[width=60mm]{vu.png}\\[8ex]
Cartographic Generalization of Lines using free software \\
(example of rivers) \\ \vspace{4mm}
}
\iffalse
\fi
\author{Motiejus Jakštys}
\date{
\vspace{10mm}
Version: \VCDescribe
\VCDescribe
}
\begin{document}
@ -55,7 +52,7 @@
Current open-source line generalization solutions have their roots in
mathematics and geometry, and are not fit for natural objects like rivers
and coastlines. This paper discusses our implementation of \WM algorithm
and coastlines. This paper discusses our implementation of {\WM} algorithm
under and open-source license, explains things that we would had
appreciated in the original paper and compares our results to different
generalization algorithms.
@ -71,16 +68,70 @@ Current open-source line generalization solutions have their roots in
\section{Introduction}
\label{sec:introduction}
A number of cartographic line generalization algorithms have been researched,
which claim to better process cartographic objects like lines. These fall into
two rough categories:
When creating small-scale maps, often the detail of the data source is greater
than desired for the map. This becomes especially acute for natural features
that have many bends, like coastlines, rivers and forest boundaries.
To create a small-scale map from a large-scale data source, these features need
to be generalized: detail should be reduced. However, while doing so, it is
important to preserve the "defining" shape of the original feature, otherwise
the result will look unrealistic.
For example, if a river is nearly straight, it should be nearly straight after
generalization, otherwise a too straightened river will look like a canal.
Conversely, if the river is highly wiggly, the number of bends should be
reduced, but not removed.
Generalization problem for other objects can often be solved by other
non-geometric means:
\begin{itemize}
\item Towns and cities can be filtered and generalized by number of
inhabitants.
\item Roads can be eliminated by the road length, number of lanes, or
classification of the road (local, regional, international).
\end{itemize}
Natural line generalization problem can be viewed as having two competing
goals:
\begin{itemize}
\item Reduce detail by removing or simplifying "less important" features.
\item Retain enough detail, so the original is still recognize-able.
\end{itemize}
Given the discussed complexities, a fine line between under-generalization
(leaving object as-is) and over-generalization (making a straight line) must be
found. Therein lies the complexity of generalization algorithms: all have
different trade-offs.
\section{Literature review}
\label{sec:literature-review}
A number of cartographic line generalization algorithms have been researched.
The "classical" ones are {\DP} and {\VW}.
\subsection{{\DP} and {\VW}}
\cite{douglas1973algorithms} and \cite{visvalingam1993line} are "classical"
line generalization computer graphics algorithms. They are relatively simple to
implement, require few runtime resources. Both of them accept only a single
parameter, which makes them very simple to adjust for different scales.
However, both of them are emitting insufficient
\subsection{Modern approaches}
After {\DP} and {\VW} have been established,
These fall into two rough categories:
\begin{itemize}
\item Cartographic knowledge was encoded to an algorithm (bottom-up
approach). One among these are \cite{wang1998line}.
\item Mathematical shape transformation which yields a more
cartographically suitable down-scaling. E.g. \cite{jiang2003line},
\cite{dyken2009simultaneous}, \cite{mustafa2006dynamic},
\cite{nollenburg2008morphing}.
\item Mathematical shape transformation which yields a more cartographic
result. E.g. \cite{jiang2003line}, \cite{dyken2009simultaneous},
\cite{mustafa2006dynamic}, \cite{nollenburg2008morphing}.
\end{itemize}
During research for the mentioned articles, prototype code has been written for
@ -113,10 +164,8 @@ those through a widely available \cite{chaikin1974algorithm} smoothing
algorithm via \href{https://postgis.net/docs/ST_ChaikinSmoothing.html}{PostGIS
ChaikinSmoothing}.
\section{Visual comparison}
\subsection{Comparison algorithms and parameters}
\subsection{Combining bends}
\section{Methodology}
\label{sec:methodology}
\section{Conclusions}
\label{sec:conclusions}

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