PATTERN CLASSIFICATION APPROACHES TO MATCHING BUILDING POLYGONS AT MULTIPLE SCALES

Transcription

1 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume I-2, 2012 XXII ISPRS Congress, 25 August 01 September 2012, Melbourne, Australa PATTERN CLASSIFICATION APPROACHES TO MATCHING BUILDING POLYGONS AT MULTIPLE SCALES Xang Zhang a, b, *, X Zhao a, b, Marten Molenaar b, Janten Stoter c, Menno-Jan Kraak b, Tnghua A a a School of Resource and Envronmental Scence, Wuhan Unversty, Chna - tnghuaa@gmal.com b ITC, Unversty of Twente, the Netherlands - {xzhang, xzhao, molenaar, kraak }@tc.nl c Delft Unversty of Technology, the Netherlands -.e.stoter@tudelft.nl Commsson II, WG II/2 KEY WORDS: Data Matchng, Mult-Scale Modelng, Map Generalzaton, Pattern Classfcaton, Buldng Feature ABSTRACT: Matchng of buldng polygons wth dfferent levels of detal s crucal n the mantenance and qualty assessment of multrepresentaton databases. Two general problems need to be addressed n the matchng process: (1) Whch crtera are sutable? (2) How to effectvely combne dfferent crtera to make decsons? Ths paper manly focuses on the second ssue and vews data matchng as a supervsed pattern classfcaton. Several classfers (.e. decson trees, Nave Bayes and support vector machnes) are evaluated for the matchng task. Four crtera (.e. poston, sze, shape and orentaton) are used to extract nformaton for these classfers. Evdence shows that these classfers outperformed the weghted average approach. 1. INTRODUCTION Geospatal data are usually collected for the same geographc areas from dfferent sources and/or at dfferent scales, and for dfferent purposes. To make best use of dfferent data sources, e.g., to carry out advanced spatal analyss based on dfferent abstracton levels (Tmpf et al., 1992; Lüscher et al., 2009), matchng between datasets s needed. On the other hand, to fulfll the ncreasng and dverse demand of spatal data at varous resolutons and scales, detaled spatal databases are beng bult or under constructon va generalzaton n many countres, from whch smaller scale representatons can be derved. However, snce fully automated generalzaton s to date not avalable, multple representaton databases (MRDBs) became a compromse (Hampe et al., 2003; Sarakosk, 2007). That s, spatal data of dfferent levels of detal are stored smultaneously and updates are propagated across scales. In ths process data matchng s key to establshng lnks between correspondng obects for the mantenance (Klpelänen, 2000). Addtonally, to automatcally assess the qualty of generalzed obects wth respect to ntal ones, lnks between correspondng obects are also requred (Stoter et al., 2009). Matchng spatal obects from two heterogeneous datasets s a complex decson process. To decde whch pars of obects match or smlar, we need dfferent smlarty measures and complex reasonng. Two fundamental problems arse. Frst, what are the key crtera (or varables) that help determne the matchng? Second, how can we make a decson based on the multple crtera? Prevous work has been dedcated to the development of new smlarty measures. In general, those measures can be dvded nto geometrc, semantc and contextual measures. For nstance, Beer et al. (2005) developed spatal on algorthms that match ponts only usng ther locatons. To match more complex obects (polygons and networks), other geometrc nformaton such as angles, shapes, topologcal propertes are also used (Walter and Frtsch, 1999; Gösseln and Sester, 2004). Some other matchng approaches also compare the semantcs of obects, especally names (Ramond and Mustère, 2008), provded that the attrbute was collected for the datasets. A remarkable approach, proposed by Samal et al. (2004), measures the contextual smlarty between two buldngs. The context (.e. surroundng landmarks) of an obect s captured n a proxmty graphs, and the contextual smlarty s calculated between two graphs usng dsplacement vectors. In vew of ths, Km et al. (2010) represent context (also landmarks) by a trangulaton structure, where the contextual smlarty s measured based on areas and permeters of the trangles organzed around the buldng. Ths method s more relable n case of large dscrepances exstng between matchng datasets; a lmtaton s that the matchng of landmarks reles entrely on names, whch s less applcable snce names are not always avalable n topographc data. Note that, to use context one should ether match the context, as dd n Samal et al. (2004), or refer to a unque context to whch both datasets refers. On the other hand, combnng varous matchng crtera nto a decson s stll a challenge. Approaches based on a sngle crteron (e.g. Km et al., 2010) are free from ths ssue. However, sngle source of nformaton does not provde enough evdence for a relable decson. Therefore, we clam that data matchng should combne multple sources of nformaton as evdence to mprove the matchng. Ths paper ams to tackle ths multvarate decson problem. A straghtforward approach to ths s weghted average. Ths approach s commonly used (e.g. Walter and Frtsch, 1999; Samal et al., 2004) and conssts of two steps: (1) normalzng measured values, and (2) assgnng weghts to dfferent measures. Clearly, both steps can be problematc. For one thng, normalzaton factors may not always be avalable. For another, manual weghtng s usually subectve; even experts may sometmes fal to assgn approprate weghts. Addtonally, as * Correspondng author. xzhang@tc.nl; xang.zhang@whu.edu.cn 19

2 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume I-2, 2012 XXII ISPRS Congress, 25 August 01 September 2012, Melbourne, Australa data matchng s essentally an uncertan process, crsp decsons would nevtably reduce the matchng performance. To address these ssues, we ft the data matchng nto a pattern classfcaton framework, whch are partcularly effectve n solvng multvarate decson problems. One advantage s that the model parameters can be learned from avalable data, and the subectve weghtng can hence be avoded. Moreover, some of the classfcaton methods (e.g. probablstc ones) can handle uncertantes, whch may help to mprove decsons n ambguous stuatons. The remander of ths paper s organzed as follows. Secton 2.1 formalzes the data matchng nto a pattern classfcaton problem; then basc geometrc crtera and supervsed classfers are brefly descrbed n Sectons 2.2 and 2.3. An extenson s presented n Secton 2.4 whch ntegrates soft classfcaton and doman knowledge to mprove the matchng. The classfers are evaluated and dscussed n Secton 3. Ths paper ends wth conclusons n Secton 4. where d and g represent buldngs n dfferent datasets. Ths sze smlarty s nterpreted as follows: when SzeSm() more approaches to 1, the two buldngs are more smlar n sze. Shape of buldngs s characterzed by shape ndex (Peter, 2001) whch s formally defned: Permeter( p ) ShapeIndex( p ) 2 Area( p ) where p s a polygon. Shape ndex measures the complexty (compactness) of shapes wth respect to crcle. The rato of shape ndex s used to compare the relatve complexty of two shapes: (2) ShapeSm( d, g ) ShapeIndex( d ) ShapeIndex( g ) (3) 2. DATA MATCHING AS PATTERN CLASSIFICATION 2.1 Problem Formalzaton Data matchng ams to fnd all possble correspondence pars from two datasets based on several crtera. Each crteron compares a specfc characterstc (e.g. shape or orentaton) between a par of obects and yelds a measured value. Based on the measured values a decson can be made as to whether ths par of obect matches or not. In the followng, we formalze ths problem as a pattern classfcaton problem. Let r = (d, g ) D G be a relaton or par of obects, where d D and g G are obects n detaled and generalzed data. Data matchng can then be vewed as a two-category pattern classfcaton problem wth category C = { Matched, UnMatched }. In other words, r can be classfed nto a category c k C, dependng on the feature vector or measured characterstcs (f f 1,..., f n ). Formally, there exsts an unknown functon g D GC that maps an nput pattern (r ; f) to a category label c k. However, snce such an deal functon s not avalable for real applcatons, most classfcaton approaches learn from tranng patterns TP = {(f 1, c 1 ),..., (f n, c n )} and produce a functon h that approxmate g as closely as possble (supervsed learnng). 2.2 Basc Crtera Four crtera,.e., poston, sze, shape, and orentaton smlarty, are used based on commonsense knowledge to show the potental of classfcaton based matchng. These crtera are measured from pars of buldngs (.e. d and g from detaled and generalzed datasets). Frst, poston smlarty s measured based on dstance between buldng centrods. Second, we defne sze smlarty based on the followng sze rato: SzeSm( d, g ) Area( d ) Area( g ) (1) The measure of buldng orentaton s based on wall statstcal weghtng (WSW) descrbed n Duchêne et al. (2003). The resultng orentaton s wall drecton α n [0, /2]. The result also comes wth a confdence value (numbers ndcated n Fgure 1a), whch s calculated by countng the proporton of length of the edges that orent to α σ (a tolerance). Typcal buldngs have two perpendcular wall drectons (α and α + /2). The walls of drecton α + /2 also add to the confdence value of the resultng WSW orentaton α (e.g. confdence values of A - E n Fgure 1a approach to 1). Fgure 1: Buldng orentaton measures: (a) wall statstcal weghtng; (b) adaptaton n ths approach (output orentatons are n bold lnes wth confdence values numbered upper-rght) In ths paper, we adapted the orgnal WSW, n whch we dstngush wall drecton α from α + /2. The output orentaton s the drecton (n [0, ]) n whch lengths of walls accumulate most; n most cases ths s the domnant one (.e. maor wall drecton) of the two perpendcular drectons. After adaptaton the output orentatons adust better to ther maor wall drectons (e.g. A, B, C n Fgure 1b). Confdence value decreases accordngly n our adapton snce walls of drecton α + /2 do not add to walls of α (e.g. A - E, H n Fgure 1b). The adapted confdence value s now correlated wth the degree of elongaton (strength of maor wall drecton). A square (E) wth a weak maor wall drecton has a low confdence (0.5); an oval (F) wth a strong maor wall drecton has a relatvely hgher confdence (0.57). Note that ths adapton s by no means to descrbe a general orentaton, but to choose from among the wall drectons the most sgnfcant one (n [0, ]). However, except for round shapes (G) the adapted WSW s suffcent for measurng the smlarty of buldng orentatons even n the case of star-lke shapes (D). After generalzaton, star-lke shapes should reman ther wall drectons to keep ther characterstcs, but ther general orentatons may change. 20

3 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume I-2, 2012 XXII ISPRS Congress, 25 August 01 September 2012, Melbourne, Australa Dev( d, g ) WSW( d) WSW( g ), (4) Dev( d, g ) Dev( d, g ), f Dev( d, g ) / 2 In Equaton (4), we defne smlarty by orentaton devaton between two buldngs. The smaller the devaton s the two buldngs are more smlar. of classfcaton error) and the kernel parameter should be found for an optmal classfcaton. The optmal combnaton was automatcally learnt from tranng data usng LIBSVM 1 package wth a 10-fold cross-valdaton. Hence, we use SVM C, to denote dfferent combnatons of parameter values. 2.4 Incorporatng Doman Knowledge The generalzaton knowledge can be used to mprove the classfcaton results. Note that the knowledge can only be ntegrated wth classfers that can handle uncertantes (e.g. Nave Bayes). There are two basc rules: - Rule 1: any generalzed obect should lnk to at least one ntal obect; - Rule 2: any ntal obect should lnk to one most probable generalzed obect. Fgure 2: Intal and correspondence buldngs wth strong and weak maor drectons Consderng the fact that some buldngs have very strong maor wall drectons (e.g. the ntal buldng n Fgure 2) and others may have very weak ones (e.g. the target buldng n Fgure 2), calculaton based on Equaton (4) may result n an orentaton devaton /2, ndcatng that the two are very dfferent n orentaton. Ths s however not true as shown n Fgure 2. To better account for ths, we use a confdence threshold T C to dstngush between strong and weak maor wall drectons. If the confdence value s less than T C the buldng s regarded as havng weak maor wall drecton, and vce versa. Further, f at least one of the matchng canddates has a weak maor wall drecton, the orentaton dfference between these two should not exceed /4; only f both buldngs n the par have strong maor wall drectons, the orentaton dfference s based on Equaton (4). The new functon s defned as follows: / 2 Dev( d, g ), OrDff( d, g ) Dev( d, g ), f Dev( d, g ) / 4 (Con( d ) T Con( g ) T ) otherwse Con() s the confdence value of obect orentaton. In ths study, T C = 0.55 was emprcally determned from tranng data. 2.3 Supervsed Classfers In ths secton, we brefly descrbe the supervsed classfers we tested n ths research. These are classfcaton and regresson tree (CART) (Breman et al., 1984), C4.5 algorthms (Qunlan, 1993), Nave Bayes classfer (a probablstc model) and Support Vector Machnes (SVM). Detaled treatments can be found n Duda et al. (2001). Dfferent parameters that are used to tune the above-mentoned classfers are brefly ntroduced here. Frst, for decson trees, we used a rule for CART to stop splttng when the maorty class rate reaches MR(%). So we use CART MR to denote ts dfferent versons, wth CART * denotng no stoppng rule appled. Then, a Radal Bass Functon (RBF) kernel s used for SVM, where the best combnaton of C > 0 (penalty parameter C C (5) Fgure 3: lnks between ntal (whte nodes) and generalzed (dark nodes) obects We further dstngush between three ambguous stuatons (Fgure 3) where the above-mentoned rules are volated: A. No ntal buldngs s lnked to the generalzed buldng (ths buldng s called solated node); B. In a cluster (group of obects connected by the lnks), ntal buldngs have more than one lnk to generalzed buldngs; C. Smlar to case B; but dfferently, only one ntal buldng s lnked to the generalzed buldng, creatng a sngularty. These stuatons can be mproved by the followng step-by-step refnement: 1. Lnk every solated node (Fgure 4a) wth the most probable canddate; 2. For each cluster, f there s no sngularty (Fgure 4b), select the most probable lnk from ntal buldngs and remove less probable ones; 3. Otherwse, for each dentfed sngularty s, cut all lnks from ntal buldng d except for the lnk between (d, s ) and update the cluster; 4. Repeat step 2 and 3 untl none of the above tree stuatons can be found. 3. EXPERIMENTS AND DISCUSSION We mplemented the descrbed work as follows. Frst, the four measures were mplemented based on GenTool an nteractve generalzaton and evaluaton system developed by a group of colleagues at Wuhan Unversty, Chna. Tranng samples were generated n GenTool and exported to classfers. The classfers were mplemented usng thrd party software packages. Specfcally, Nave Bayes classfer and CART were realzed n MATLAB software 2 ; C4.5 was mplemented based on the code provded by Dr. Ross Qunlan 3 (nventor of C4.5); 1 LIBSVM: 2 MATLAB 7.8 (R2009a):

4 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume I-2, 2012 XXII ISPRS Congress, 25 August 01 September 2012, Melbourne, Australa LIBSVM package was used for SVM mplementaton. Addtonally, an nteractve matchng toolbox and a weghted average approach were mplemented to help human operators generate tranng data and to compare the performance of the classfers wth respect to the weghted average approach. 3.1 Tranng Data The datasets to be matched are Dutch topographc datasets at 1:10k and 1:50k (.e. TOP10NL and TOP50vector, Kadaster). Four datasets were used here,.e., TOP10NL and TOP50vector at study area A and B, respectvely. Study area A represents an area whch s characterzed by suburbs and rural settlements mxed wth a small porton of towns; whereas study area B shows a rather pure characterstc whch s domnated by small towns. Another am s to show whether dfferent characterstcs of data nfluence the matchng accuracy. The tranng samples were generated by experts and are summarzed n Table 1. No. of buldng Scale Scale 1:10k 1:50k Tranng sample No. of pars (Matched UnMatched) Area A ( ) Area B ( ) Table 1: Overvew of topographc data and tranng samples 3.2 Evaluaton Procedure and Crtera The followng experments were carred out. Frst, the classfers were traned wth one tranng set (ether A or B), and tested wth the same set or usng a 10-fold cross-valdaton (results are not shown due to lmted space). Second, to show how well the traned classfers can be appled to classfy novel patterns (unknown data), we traned the classfers wth sample A and tested wth B, and then reverse. Ths way we also get nsght nto whether the predcton power of the classfers reles on spatal characterstcs. To classfy unknown data, for each source obect at the target scale we select canddates n the ntal data that falls nto some radus (R) of the source; R was emprcally determned to cover potental canddates for a gven dataset. Multple matched canddates are condtonally regarded as n-to-1 matchng (see Secton 3.4). Dfferent versons of the classfers were evaluated, ncludng CART, CART 95%, CART 85%, SVM 0.5,2 (see Secton 2.3). The crtera used to evaluate the performance of these classfers are precson and recall. Besdes, tree sze s used to evaluate decson trees. 3.3 Classfcaton Accuracy To summarze, tranng a classfer and testng t wth the same data obtaned hgher precson and recall than tran t wth one and predct on another. For example, C4.5 obtaned 87.7% precson and 88% recall, whch s better than ts performance shown n Table 2, to name but a few. CART *, n partcular, obtaned about 94% precson and 96% recall when traned and tested wth the same data. Ths probably means an over-ftted model. Settng 1: classfer traned wth A and tested wth B Classfer Precson [%] Recall [%] Tree sze [leaf no.] CART CART 95% CART 85% C NB N/A SVM 0.5, N/A Settng 2: classfer traned wth B and tested wth A Classfer Precson [%] Recall [%] Tree sze [leaf no.] CART CART 95% CART 85% C NB N/A SVM 0.5, N/A Table 2: Performance of dfferent classfers and for two settngs Precson Recall Weghted average 61.7% 61.7% Table 3: Performance of weghted average approach on study area A wth normalzed and equally weghted measures The predcton capablty of the traned classfers on new data s shown n Table 2. Table 2 shows that most classfers worked satsfactorly for both settngs and outperformed the weghted average approach (Table 3), expect for CART *. In general, decson trees provde more nterpretable results (.e. rules) than numercal learnng. In addton, Table 2 confrms that hgher performance n classfyng new data s correlated to relatvely smaller szes of generated trees. Among other decson trees, C4.5 appears to be the most promsng n ths matchng task due to ts better performance, ts stablty n reversng tranng and test sample and ts more tractable tree szes. CART * performed poorly because t over ftted the tranng samples (see also our dscusson n the prevous paragraph) and generated over complcated trees, whch not only makes the resultng rules more dffcult to nterpret but also reduces ther performance n classfyng novel patterns. Concernng C4.5, NB and SVM (C = 0.5, = 2 automatcally computed for tranng samples), no persstent concluson can be drawn as to the dfference n ther performance. It s however known that classfcaton accuracy of NB classfer can be further mproved (Secton 3.4). Besdes, hgher precsons and lower recalls can be observed for the classfers traned wth dataset A (characterzed by a mxture of towns, suburb and rural settlements) and tested wth dataset B (characterzed manly by towns) compared wth the reverse settng. Note that both tranng sets were carefully prepared to gan the same postve class rate (Table 1). Ths ensures that such a dfference was not caused by dfferent postve class rates of the tranng samples. Ths suggests that spatal characterstcs of the data have an mpact on classfcaton performance but not too bg. However, how dfferent characterstcs may affect the matchng accuracy needs to be further nvestgated. 3.4 Improvement by doman knowledge (a) C4.5 (b) Nave Bayes (c) SVM 0.5, 2 Fgure 4: Matchng examples predcted for sample set A by tranng from set B (lnks are shown n red between ntal and generalzed buldngs) 22

5 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume I-2, 2012 XXII ISPRS Congress, 25 August 01 September 2012, Melbourne, Australa A closer look at the matchng results gets nsght nto where and why msclassfcatons occurred. Several typcal poor matchng was dentfed: - Some pars of buldngs (should be matched) were unmatched because ther shapes are drastcally dfferent gven that they are very close (e.g. A n Fgure 4a and 4b); - Some pars (should not matched) were msmatched because ther shape, orentaton and poston are too smlar (e.g. B n Fgure 4b); - Incorrect matchng between groups of buldngs where n:m relatons are lkely to happen (e.g. C n Fgure 4a-c); n ths case, contextual nformaton should mprove the matchng. Some of the above msclassfcatons can be mproved by ncorporatng the generalzaton knowledge as descrbed n Secton 2.4. For example, the poor stuatons A, B, C n Fgure 4b) by Nave Bayes are mproved n the followng way. Case A: Two related pars are searched n the probablty table (Fgure 5); the generalzed obect (#2985), wth two potental lnks that were labeled by NB as UnMatched. Accordng to Rule 1, a lnk wth relatvely hgher postve probablty s selected as best ftted lnk. The selected buldng (#8682) proves to be the correct correspondence. Fgure 5: Probablty table for case A Case B: One ntal buldng (#10450) has two correspondences n generalzed dataset, whch volates Rule 2. Snce no sngularty s found n ths cluster, Rule 2 can be appled drectly by removng one of the lnks and the most probable lnk s selected (Fgure 6). The selected correspondence (#2984) s the upper one n the cluster B n Fgure 4b, whch s a more reasonable result. Fgure 6: Probablty table for case B Case C: It s more complex as a sngularty s detected (the rght most one n cluster C n Fgure 4b). The detected sngularty (#3008) lnks to the buldng (#10805) n ntal dataset (Fgure 7a), therefore ths lnk has to be kept. Meanwhle, the other outgong lnk from #10805 should be removed accordng to Rule 2, though t appears to be a more probable lnk for # After ths, the matchng result s as follows (Fgure 7b), and surprsngly ths s exactly what the manual matchng was lke, even wthout the use of contextual nformaton. (a) (b) Fgure 7: Probablty table for case C (a) and the result after doman knowledge s consdered (b) The par-wse matchng allows for n-to-1 and n-to-m relatonshp to be mplctly modeled (e.g. {{a1,b1}, {a2,b1}, {a2,b2}, {a3,b2}} forms a 3-to-2 relatonshp). However, current use of doman knowledge (Rule 2) as a post-process s to detect and remove ncorrect relatonshp such as the group C n Fgure 4b, whch naturally dsallows n-to-m relatonshps (although n-to-1 s stll allowed). Better rules are requred to replace Rule 2 n order to dstngush ncorrect correspondence and potental n-to-m relatonshps. A pror matchng of buldng groups as descrbed n Zhang et al. (2010) may be helpful. In summary, classfers wth probablty structures and soft decsons are more promsng n the matchng as doman knowledge can be ncorporated to mprove the performance. As descrbed above, the matchng results obtaned from Nave Bayes can be mproved by further analyzng the probabltes usng the doman knowledge (Secton 2.4). Tradtonal SVM as used here only provdes crsp decsons. However, f a probablstc SVM (Platt, 1999) s used, the doman knowledge can also be ncorporated to mprove the SVM-based matchng. 3.5 Reflecton on the matchng crtera Ths paper presents a frst attempt nto the classfcaton-based approach to data matchng where multvarate decson s mportant. So the selecton of optmal crtera (and measures) to acheve the best possble matchng results was not the focus. Four categores of crtera (poston, sze, shape and orentaton) were used based on commonsense knowledge. A correlaton analyss (as n Werder et al., 2010) was later carred out whch shows no sgnfcant correlaton between the four measures. However, one should note that the categores are by no means complete and the measures used to evaluate the crtera may not be the optmal ones. For example, t s questonable whether to use the sze rato for the matchng because dfferent sze ratos can be caused by enlargng smaller obects, though a dstrbuton of sze ratos can be learnt whch may facltate the classfcaton. To get more nsghts, we carred out parallel experments where the sze crteron was removed. For settng 1 we found that for C4.5, NB and SVM precson decreases and recall ncreases, ndcatng that whle more true postves (correct lnks) were found, even more false postves were also produced, whch s arguably undesrable. For CART of dfferent versons both precson and recall decrease. Smlar results were obtaned for settng 2. Ths suggests that the sze crteron adds more or less to the matchng. However, a redesgn of sze crteron n the future takng nto account the possble change rato n relaton to ntal szes may gve more dscrmnatng power. Lkewse, by removng shape respectve orentaton crtera, obvous decrease n both precson and recall occurs for the classfers. Ths suggests that the used measures are relevant for buldng matchng though better performance can be antcpated by desgnng measures that dfferentate specal cases (e.g. oval shapes). By teratvely removng and addng matchng crtera and measures we get an mpresson of ther relatve contrbutons to the matchng. Our experment shows that dstance crteron was the domnant parameter for all classfers, whle the contrbuton of e.g. sze and shape vared for dfferent classfers. However, t s unknown yet whether t s ustfed to use ths approach to study the relatve weghtng of model parameters. Also as we 23

6 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Scences, Volume I-2, 2012 XXII ISPRS Congress, 25 August 01 September 2012, Melbourne, Australa argue prevously, explct weghng by desgner should not be a problem n the supervsed classfcaton approach. In future research, more measures should be analyzed for dfferent crteron categores, the optmal one or combnaton of ones can be chosen usng technques such as prncpal component analyss (Burghardt and Stenger, 2005). Further, other crteron categores such as semantc and contextual ones can be ntegrated to mprove the data matchng. 4. CONCLUSION Fttng data matchng process nto a pattern classfcaton framework ams to provde a more generc approach to the matchng of spatal obects (polygons, lnear features, networks, etc.). In ths framework, combnng multple crtera nto fnal decsons s more effectve and adaptve: rather than arbtrary normalzaton and weghtng, model parameters can be learned from tranng data. Four classfers (CART, C4.5, Nave Bayes and SVM) wth dfferent parameter values were tested to show ther possbltes n matchng buldng polygons. They outperformed weghted average n terms of classfcaton accuracy. Generally, the accuracy (both precson and recall) reached approxmately 80% and hgher, based on four smple smlarty measures (.e. poston, sze, shape and orentaton). To further mprove the matchng result, advanced measures lke semantc and contextual smlarty should be consdered. Moreover, classfers that can handle uncertantes could be further mproved by ntegratng doman knowledge. REFERENCES Beer, C., Doytsher,Y., Kanza,Y., Safra,E. and Sagv,Y., Fndng correspondng obects when ntegratng several geospatal datasets. In: Proceedngs of the 13th ACMGIS, pp Breman, L., Fredman, J. H., Olshen, R. A., and Stone, C. J., Classfcaton and regresson trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software. 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