From 12d54955fd7ee84ac51aced12bcc5a1fc4af0bbe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Motiejus=20Jak=C5=A1tys?= Date: Thu, 1 Apr 2021 09:42:08 +0300 Subject: [PATCH] text and logo --- IV/mj-msc.tex | 83 +++++++++++++++++++++++++++++++++++++++----------- IV/vu.png | Bin 0 -> 36101 bytes 2 files changed, 66 insertions(+), 17 deletions(-) create mode 100644 IV/vu.png diff --git a/IV/mj-msc.tex b/IV/mj-msc.tex index e2e0392..186e595 100644 --- a/IV/mj-msc.tex +++ b/IV/mj-msc.tex @@ -33,18 +33,15 @@ \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} diff --git a/IV/vu.png b/IV/vu.png new file mode 100644 index 0000000000000000000000000000000000000000..19870c3252c31273bc3165b48128e3e3d7b7cd5b GIT binary patch literal 36101 zcmdSBc{r5)`!}wH%2o+QjP5LDFFPT1mn2jOSxZRveHk-jDZ(A4B6}fZ-;KeHC8@@~ z4Q3ci))_Mx3}$;S-JkCt&-eHqzrTLZaXe2)b#;xo-plzum)H3^&#U-*riQ%7MUJzv zvGE$+{`&zN8#|Sa?QqmlPT-SM3ARPR<(Thnn*cU8#R~_24uyWv4`ySNU^DvLz#?># zIMwPeYB;&G;gJ*W{{VdWMJv}ec%7E5<)roawaim@3hwznwK9>o_)%8p)agg@ZCm%9 zWF0KOrdR%&In;6`^vGYyPSZ}L%c8w!ojBR_uhC(a^63&t%`+Wv1R1IGlVS(V&&K8y zz~GPj^Dnjz-9J|e9Q&W^e{NrZ|J+@JbN#vg@3)b@{C_?@3hWH{_1|t!kPiL%cvk$+ z<-gtj&(r;9_WyZ0w*Sojf5mkFwb}o9cmLV!e-q|^yZzsV`TxDU|6|krN7Vl{{{Mfn z{hxRDe{8z{2Q;T*kmY}r4aea=7c3Pd^?_wti%ZtJ()OKcLX`@NPK-ze_rE=KU`0-~ z(fi)1H21{JASnnRQva>0_r}Mt^)>fY8Y!9(Lew%1?TYjmOba|=e+w{7;E9IJoxLl2v#GqIrBes3sG6U`}@%a4$^P|-%zz#ECsZrU+Tr~Efh6=9Ja-4?{ z7oEpt5?MHww z9!z>Gx^Cc~un5tcw<$)0lI0&53;yg^7oDoV%=k~$30Xr&tSq|2Z zR}AL$AuZ8~5+*p`_@clmgRoOCw;Pg$S*C`8_ygZGE8aF)5Fg+^7i-)D?G~E{dxYPq z6uFw_665D4HtV1;HidQSa_}8|Szz!EJ9X{mVc?kt9uHC!O?Z`-ax25@Hbz>NwhV6k zZLU=h>-4AkWoT-+fmiBpAIBu|u5u0qKG2kz;(puo20pG})KXBY9NKsx@Pt02ohoW6N7L#;yA9qpCVt6#= zZBtRrzpFFkH$vx^Y3~wBUX~a{<+!@h2uTy43;NyX3f<>!sec4UI2c0VbX@OrLL+Qw zJy=F9jVkzIzjR_J)Fj3$q@9wxRJJld@z1_N7kTXOh}OoG9HV{f0EVuSlI8m~vy0e& z4`Wv|a}VrG}r=t4#QWYl`X= zBnRQY9$SX=1~XC5PQqz})S_8(#sU=h{eA%{x56b@yDvQGl_VTA{~sM;)UHCe(LGG1 z=dUoy*9l3BX-j0ah>FtprAl^7J}TtS3W{N*#_&**%twPmW1fAQ z{U&=-sK!}HA2lCa*=?bHMK5^LMLSSEah`W z-$1fQ1-2<_{&a#H$uMK8gH*gAA6_|2Zc=nMBA0g}=am3$J#aCUJr09_w+)1ye&Hhv zSMFJTSRxn2i>?G~FUG}4zQ-P3{ zyW0o)2E+?eQjll7a9hPtU|YYX`Ttf6z;nYGy@)Aps(v|7@o*-M6Po7rFVUYWd5td9UMR|qnaGU60 z@zz*-dwWs+XJNS%PJ>|iU6`qWMtys3-|;28qPacD$f#_rn0-Yzc@qPGJ0PCkEnlYTv-}2S!)_YdpmAeGI4$K0y;bZsiB} z8MUZf5TS&f51w}ZC$PTblRG@w?NgEQx%^%AIPU2EJj!QtKgBIch|G%ND3xQ}>aymR zf(fo_*10KRNk_i)qUR+8!=XWioESjhfU^nF8z+}sv`=>X3LFLc@YDtSDXe|UElevP zUIe2sb=oI>bB!Y9gU9$U>@}Ch(m2@9_@tegdBslYmZzH2L;zR7A8cx;N^f2L7Q-fE zL+TzRhvY0EeUih%hnFP&=t!INnM;$TyF>Z%R7EtSnnOEx0qQin`WJlKH+}}rgOZ5o zo6jT5D1e&p->y$zWF9{sVksk-RzJD#-M67zu?qo)<+34#Sn?e1`oKMZlmAIAiHo~~ z+2cG(ILk@TmVy}q^8k*E*6wX;wgbEFAha*C6i^LX8p zUH;_$>VyfRIX6~@SKPu(QZ>O$yTMlW&GJR5Q4AY6;+@$QG{Y|gUMmqux~*+0uq5;X z1DBmGGjML&Woj$r5(|*)Z{;@P3DJ1o^sUoy7TKAr0t_@Oer>idRUJ!Yymd`G zNh}@rV8|bB6zlCfnwFH|$>nw^01;I=Rh>{0Q&BMG!YSj)OYcby^biSs1(+Y9xgqB0 z61@rVJb>e|Agsh1lWaNZ&f#BL*&Y1*rWUQfulP}0?(!H^FQxv@*i-@d*c3Tc=T4pgHG3YHP{6!k}+D1%6^c^047w=QKfm+ zzWCzn+`5+B9FDI^_E)PSDr zoAF??u20FC0S?TQ_7Pw5oK1g1vI|vqZdfXDXHaa@`D@v5neCS|(w^^O%*^a9Z zpNY#BGwK#X_{?d&2bafbw`fy0BPi+~)75VrWfVvC%O90IUh;XTNC#P#Rr{PGhR89c zO{Exb7Lb_>(=JW;4_o;h`{^iA2bX`X%QnhGekCYy1DSAkF!Rd7hw+jCQiQ1%)T~w| zx8x}HGyF93_E7$&92%0cfW$mW>_m#lTUI+9Qarv(YoHzAVE`ADzbOCwGH*b@<6^Kt zaG0ReTmWf4+DV)m@bS^hNS~9-Hx&1g4K_I(OgYSw6z~jin2qkA^5~jdn+UhRVqzoC zi@fuy%qe)c+|pvy?A3RZdlwXwxKlO@`gi=^+6kkgi7U=V;Xbd?8L>fW(%zF##$W{# zimQg6at)(Pk0+jy9c3GSWlpb~zv_$#iO2rQLxH6|ANsbPmhF`*u+GSv_@V0847egG zNJAC#iV4oCeY+chm>Pd&b<&2E>}5|gNsI~4wF06$aA>%|Dv(il<&%ci#;3&@7DqB( zifws0NrArZSG=Q50#s zRFoXOU!72nyl$esObrSeV*$~4=-Yz(oWBv?lkJHTVtP=acjQT}N!qUM${4T3goI^; 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