FROM CLASSIFICTAION RESULTS TO TOPOGRAPHIC MAPS

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1 FROM CLASSIFICTAION RESULTS TO TOPOGRAPHIC MAPS Jochim Höhle Alborg University, Deprtment of Development nd Plnning, Denmrk KEY WORDS: Clssifiction, Topogrphic Mpping, Crtogrphic Enhncement, Assessment, Accurcy ABSTRACT: The clssifiction of high resolution multispectrl eril imgery enbles very high themtic ccurcies when mchine lerning methods re pplied. The use of clssifiction results s topogrphic mps requires crtogrphic enhncement nd checking of the geometric ccurcy. Urbn res re of specil interest. The conversion of the clssifiction result into topogrphic mps of high themtic nd geometric qulity is subject of this contribution. After reviewing the existing literture on this topic methodology is presented. It hs the gol to chieve high crtogrphic qulity nd geometric ccurcy for buildings nd other topogrphic objects. The suggested methodology for improving the clssifiction results is described. With the ISPRS dt set of the D lbelling contest lnd cover mp of six clsses ws produced nd then enhnced by the proposed method. The clssifiction used mchine lerning method pplying vriety of ttributes including object heights derived from imgery. The crtogrphic enhncement is crried out with two different levels of qulity. The user s ccurcy for the clsses impervious surfce nd building were bove 85% in the level mp. The geometric ccurcy of building corners in the level mp ws ssessed by mens of reference dt derived from DSM-bsed orthoimge. The obtined root men squre errors were RMSE_x =. m nd RMSE_y =.7 m. All processing could be crried out with high level of utomtion.. INTRODUCTION Big progress hs been chieved in the clssifiction of eril nd stellite imges by mens of mchine lerning methods. The clssifiction results, however, re not topogrphic mps. Generliztion of the mp content nd crtogrphic qulity re bsolutely necessry for topogrphic mps. The geometric ccurcy hs to meet high demnds. Urbn res re of specil interest. Importnt is lso tht these mps cn be produced quickly nd updted in short intervls of time. This demnd requires utomtic processing to lrge extent. In this contribution we focus on utomtic D mpping of urbn res with detiled content including smll objects. The positionl ccurcy of well-defined objects should be less thn one meter. The grphicl output must hve crtogrphic qulity, which includes simplifiction of the content nd representtion of mn-mde objects by stright nd orthogonl lines. Such mp dt re in need. An overview on the sttus of the mpping in the world ws recently published by (Konecny et l., 5). The uthors hve collected dt from 44 countries. In the report is stted tht in the scle rnge :5 nd bigger only 3% of the totl world re is produced by the uthorities. There exist big differences in the coverge with topogrphic mps in the mentioned scle rnge, e.g. 4.7% (Afric) nd 98.% (Europe). In some of the res covered with mps or dtbses the dt my be to 3 yers old. These fcts demonstrte the need of improvements in the production nd mintennce of such dt. Privte compnies, e.g. Google nd Microsoft, hve creted mp dt for nvigtion nd loction bsed services. They often use volunteers to crete these mps. The Open Street Mps (OSM) is one exmple. The contents nd ccurcy of such mps differs. Tests indicte tht these mps my hve geometric ccurcy of.6m fter correction of systemtic errors (El- Ashmwy, 6). The lst dvncements in the sensor technology nd processing methods yield new possibilities to fster produce nd updte the mps nd dtbses in the importnt ctegory :5 nd bigger. Topogrphic objects of urbn res need to be mpped in the scle rnge : nd bigger. These vector mps my be mnully or utomticlly derived from vrious source dt. The crtogrphic enhncement nd the geometric ccurcy re very importnt issues when topogrphic mps re the gol. This prt will be the min focus point in this work. In the pst, reserch hs been crried out in order to find solutions for this tsk. Some uthors proposed methods using lidr dt only (Gross nd Thoennessen, 6; Smpth nd Shn, 7). Awrngjep et l.,. presented method, which is bsed on eril colour imgery nd lidr. This contribution hs the gol to utomticlly produce urbn D mps of high crtogrphic qulity nd of high geometric ccurcy using eril imgery only. The structure of the pper is the following. Section describes the chrcteristics of topogrphic mps nd dtbses. The source dt nd the clssifiction methods re discussed in Section 3 nd 4. The crtogrphic enhncement of the clssifiction result is delt with in Section 5. A methodology to improve the crtogrphic qulity is presented in Section 6. Informtion on the ssessment of the themtic nd geometric ccurcy is given in Section 7. Exmples of crtogrphic enhncements re prt of Section 8. Discussion nd conclusion re in Section 9.. CHARACTERISTICS OF LARGE-SCALE TOPOGRAPHIC MAPS AND DATABASES Topogrphic mps of urbn res with mny detils re produced in scles : nd bigger. The contents of these mps depend on the purpose of the mp. Plnning nd mngement re the importnt pplictions. The vrious object types re stored in different lyers nd cn be displyed individully or in combintion. The storge in dtbses llows lso n nlysis of the mp dt. The genertion nd updting of such geogrphic informtion systems (GIS) is mjor tsk of mpping orgniztions tody. Detils on the ssessed ccurcy, the time of cquisition, nd mny other informtion re stored in metdt. Topogrphic mps re lwys georeferenced nd my lso contin elevtions. The seprtion into plnimetric

2 (D) mp nd n ccompnying digitl elevtion model (DEM) seems to be trend in mpping including updting. In the following we discuss D mps only.. D mps of urbn res D mps of urbn res re sometimes lso clled technicl mps. Such mps should hve high geometric ccurcy nd high rte of updting. They re digitl vector mps nd they re displyed on computer screen in rnge of scles, e.g. : to :. Printing of nlogue mps my occur on demnd only. The production methods re different in the world. Mnul digitizing of orthoimges is fst nd chep method to produce nd updte digitl vector mps tody. The level of detil nd ccurcy of mps produced by this so-clled hedson digitizing depend very much on the resolution of the orthoimges. The smllest object which cn be recognized should cover n re of two to three pixels which lso must hve sufficient contrst to its surroundings. The ccurcy of orthoimges should be bout two ground smpling distnces (GSD). The orthoimges re often used s bckground informtion for the mp dt in vector formt. The utomtic extrction of points, lines, nd res from orthoimges is subject of this rticle. This process is clled vectoriztion.. Objects of lrge-scle topogrphic mps Objects of lrge-scle topogrphic mps re buildings, cr ports, wlls, rods, prking lots, pths, bridges, trees, bushes, hedges nd mny others. In order to represent them on mp they should hve minimum size. This is given by the resolution of the humn eye which is bout one minute of rc. It mens tht lines of.5 mm in width cn be recognized from distnce of 3 cm. The resolution of the computer screen is given by its pixel size. For exmple, the pixel size of 56 cm screen with 68 x 5 pixels is.8 mm which corresponds to resolution of 35 pixels/cm or 9 dpi. The smllest line which cn be displyed on such screen hs width of.8 mm only. The lines of mn-mde objects must be stright nd orthogonl. The lines should be without gps nd the polygons of re objects hve to be closed. 3. SOURCE DATA OF CLASSIFICATION In this contribution the genertion of topogrphic mps is investigted by enhncing the results of clssifiction. The resources re imges, uxiliry dt nd usble fetures (ttributes) of topogrphic objects. 3. Type of imgery The imges used for the clssifiction of urbn res should hve spectrl bnds in the visible (red, green, blue) nd in the non-visible (ner-infr-red) prts of the spectrum. The rdiometric resolution should be better thn 8 bit corresponding to 56 digitl numbers. Furthermore, the imges should be metric which mens tht ccurcy vlues for the cmer constnt nd the position of the principl point hve to be determined by clibrtion. Modern eril photogrmmetric cmers will meet these demnds. The imgery hs to be tken with overlp, e.g. 6%, so tht heights cn be derived. The ground smpling distnce (GSD) should hve size tht elevtions cn be determined with sub-meter ccurcy. The tken eril imges hve to be georeferenced. This mens tht ll imges will be connected by mens of utomticlly derived homologous points. This process is clled erotringultion which requires few ground control points. Dt of sensors for position nd ttitude, simultneously recorded with the imges, will support this process nd enble ccurte orienttion dt of the imges. The eril imges cn then be trnsferred into orthoimges. In this trnsformtion, the eril imges re rectified due to their tilts nd corrected for differences in terrin elevtions. The first tsk requires the orienttion dt of the imges nd the second one needs digitl elevtion model. Two elevtion models cn be used, either the digitl surfce model (DSM) or the digitl terrin model (DTM). The DSM-bsed orthoimge depicts the buildings in correct position but their outlines re wriggly lines. In the DTM-bsed orthoimge, the outlines of buildings re shrp but displced due to the height bove ground. The DTM is derived from the DSM by mens of filtering. The size of the orthoimge pixel cn differ from the GSD vlue. Ech pixel of the orthoimge my hve coordintes of the reference system. The coordinte vlue is vlid either for the centre or for the upper left corner of pixel. In order to chieve high geometric ccurcies, the mentioned prmeters hve to be known nd be correctly used in the processing. 3. Auxiliry dt In the following procedures in clssifiction nd grphic enhncement other dt could lso be used with dvntge. For exmple, the sptil coordintes of the perspective centres nd the heights bove ground my be pplied to correct the position of buildings when DTM-bsed orthoimges re used in the clssifiction. Existing mps my be helpful to detect objects. The clssifiction cn then be restricted to certin res, e.g. to rods nd prking lots when crs hve to be detected. Other dt my be digitl elevtion models (DEMs) tht re derived from irborne lser scnning (lidr) or other sensors. 3.3 Attributes nd ttribute profiles The objects of topogrphic mps cn utomticlly be detected by mens of ttributes which chrcterize the objects. The verge height of residentil houses (dz) in suburbs my be known in dvnce. Other ttributes used in clssifiction re spectrl signture nd normlized difference vegettion index (NDVI). They cn be derived from imgery. Also ttribute profiles my be used. These re ttributes of the stndrd ttributes (dz, NDVI). The ttribute profiles re, for exmple, the stndrd devition of the intensities or of elevtions in the neighbourhood of pixel. 4. CLASSIFICATION METHODS Mny clssifiction methods hve been developed in the pst. Besides the genertion of lnd cover mps with severl clsses, the extrction of single objects is subject of mny studies. The extrction of building boundries using high resolution imges nd lidr dt is recently published in (Li et l., 3). Lidr dt re used to produce corse boundry, which is then refined by mens of edges extrcted from stereo imges. Precise 3D boundries of buildings re obtined by this methodology. In (Niemeyer et l., 4) D building outlines re generted by mens of lidr dt using elevtions nd intensities. This investigtion dels with the genertion of D lnd cover mps of six clsses using high resolution imges only. The pplied method in this investigtion is decision tree (DT). The theoreticl bckground of the DT method is given in (Breimn et l., 984). Experiences with DT clssifiction re published i.. in (Friedl nd Brodley, 997; Höhle, 4).

3 5. CARTOGRAPHIC ENHANCEMENT OF THE CLASSIFICATION RESULT In order to produce topogrphic mps from clssifiction results, severl steps hve to be crried out. Objects which re not prt of topogrphic mps hve to be removed nd topogrphic objects hve to gin crtogrphic qulity. The themtic nd geometric ccurcies of the finl result hve to be ccessed. 5. Removl of non-topogrphic objects Topogrphic mps contin permnent objects only. Crs, bots, people, nimls, tents, trmpolines, hystcks, nd other nonpermnent objects re not prt of topogrphic mps. The clssifiction results my hve inhomogeneous res representing more thn one clss. Buildings, rods, etc. hve to be represented by one colour only. Non-dt res my lso be present in the clssifiction result. These res hve to be filled. 5. Crtogrphic refinements Very smll objects like grges, oriels, sheds, cellr entrnces hve to be removed s well. A minimum size hs to be pplied, e.g., re objects should cover t lest 5 m in nture. Tht mens, tht simplifiction of the mp content hs to tke plce. The degree of this generliztion will vry for different mp types, which re chrcterized by the number of objects nd their level of detil. Mn-mde objects like buildings, wlls, rods, etc. hve to be represented in the mps or dt bses by stright lines. The outlines of buildings, e.g., consist of orthogonl nd prllel lines. Smll devitions from linerity, orthogonlity nd prllelism re esily noticeble by the mp user (buyer) nd should therefore be corrected. All of these improvements form the crtogrphic qulity. Reserch on crtogrphic refinement of clssifiction results is not very much delt with in literture. In (Li et l., ) methodology is presented. By mens of DSM building msk is derived. The corner points of buildings cn then be detected nd line prmeters re derived from them. The orienttion of the lines is then verged for whole district of the city. 5.3 Degree of utomtion The topogrphic mps hve to be compiled by high degree of utomtion. Some mnul work my still be necessry. The solutions my be different ccording to the demnds, the vilble resources, nd the skills of the personnel. Topogrphic mps nd dt bses of different content nd levels of qulity hve to be considered. Computtion times my lso be mtter of concern. Efficient lgorithms hve to be found nd pplied. 6. A NEW METHODOLOGY TO IMPROVE CARTOGRAPHIC QUALITY The results of the clssifiction my be crtogrphiclly enhnced by mens of imge processing nd imge nlysis techniques. Ech clss hs to be processed for itself. Two pproches re pplied in these investigtions. The first pproch is simple one where the focus is on high degree of utomtion, but the qulity of the mp is limited. We nme the pproch level. The second pproch, clled level, yields higher crtogrphic qulity for buildings nd other mn-mde objects, but the efforts become higher nd some interctions by n opertor re needed. The proposed solution is chieved in smll steps nd will be crried out t the prcticl tests in Section Genertion of level For the genertion of level qulity only couple of imge mnipultion re crried out (cf. Figure ). Detils for ech step re given in the following. imge of clss x diltion & erosion outlines & filling lbelling of objects computtion of fetures removl of smll objects disply & output enhnced imge of clss x Figure. Steps in crtogrphic enhncement of lnd cover mps (level ) 6.. Diltion nd erosion These morphologicl opertions re crried out by filtering. A structuring element (SE) hs to be defined beforehnd, e.g. dimond shped figure covering n re of few pixels. The mnipultions by diltion nd erosion first increses the set of pixels nd reduces them therefter. The effect is smoothing of the boundries nd removl of some noise. 6.. Outlines nd filling The first mnipultion thresholds the imge by mens of moving rectngulr window. Two prmeters hve to be specified: The size of the window nd the offset from the verge intensity within the window. The outlines of objects re generted. The second opertion genertes filling of the whole object with pixels of the intensity. The res of the objects, e.g. buildings, re then homogeneous Lbelling of objects The connected sets of pixels with the intensity cn now be lbelled by digit. The number of objects cn then be counted Computtion of fetures Fetures of objects like position, re, mximum rdius, orienttion, etc. re derived for ech of the objects in the binry imge (B). The formul for the re (A) of n object is:

4 where i, j = imge coordintes. n m A Bi, j () ij The coordintes of the centre of n object (xc, yc) re clculted by: All points of the point cloud of building boundry re mpped in the prmeter spce using combintions of θ nd ρ. The cells of the prmeter spce re used s n ccumultor which is incremented by when point stisfies the eqution (3). imge of clss b n m i j j B i, j xc A n m i j i Bi, j yc A () extrcting of point clouds The units re pixels. More detils bout these formuls cn be found in (Jin, 995) Removl of smll objects Smll objects cn now be removed using threshold for the re (A) or the rdius of n object. The result is generliztion of the mp content Disply nd output The result of three enhncements cn quickly be displyed by mens of the RGB-chnnels. Overlps between clsses cn then be discovered. In order to hve ll clsses in the mp, the imges hve to be plotted by mens of colours. The sequence of plotting should follow the rule tht hrd objects (buildings, rods, wlls) should be plotted t the end. Overlps with the soft clsses (vegettion) re then repressed. 6. Genertion of level The level pproch uses results of level nd improves the lines of mn-mde objects. Ech object hs to be processed individully. It is extrcted from the connected component imge using its lbel. The point cloud of ech boundry line hs then to be seprted from the other point clouds. The prmeters of ech line cn now be clculted by lest squres djustment. The next step is the genertion of orthogonl nd prllel lines. Corner points re then clculted by intersecting successive lines. The polygons forming buildings, cr ports, wlls, etc. hve lso to be closed. The suggested pproch is depicted in Figure for the clss building. It will be explined in more detil in the following. 6.. Extrction of point clouds belonging to lines The boundries of mn-mde objects consist of severl lines. The boundries re pproximted by stright lines. Prllel nd orthogonl lines exist t buildings, wlls, cr ports, rods, etc. The first step is the extrction of the point clouds forming the boundry lines. The seprtion of lines cn be done by mens of the Hough trnsform, which uses voting mechnism. Ech point of the point cloud votes for severl combintions of prmeters. The prmeters tht receive mjority of votes re the winners (Jin et l., 995). The lines re modelled by x cos y sin where ρ=distnce from the origin nd θ=zimuth of the norml vector to the line; x, y re constnts in the prmeter spce H(θ, ρ). (3) genertion of orthogonl & prllel lines clcultion of corner points closing of polygons enhnced imge of clss b Figure. Steps in the crtogrphic enhncement (level ) t the exmple of clss building The highest vlues in the ccumultor rry (H) correspond to the boundry lines (cf. Figure 3). The prmeters re nlysed in order to decide which point clouds hve to be extrcted so tht ll lines of the building cn be modelled. ρ Figure 3. Disply of the prmeter spce (H) of the Hough trnsform. The pixels with the highest intensities hve the prmeters (θ, ρ) of the building s boundry lines. 6.. Genertion of orthogonl nd prllel lines The extrcted point clouds of building outlines re modelled by x y c (4) i i nd coefficients (i nd ci) re now determined more ccurtely. We will use building with four corners s n exmple (cf. Figure 4). Preliminry coordintes of the corner points (x, y) re obtined by intersection of two consecutive lines (l nd l). θ

5 P P l k () l 4 l k 3 (3) P 4 l 3 P 3 If there re more thn four lines in the building the mtrix nd the vectors re extended fter the sme pttern. The unknown civlues re found by Figure 4. Sketch of building outlines nd corner points x y c c (5) c c (6) The finl coordintes of the corner points (Pi) re derived in two steps. First, the slope vlue () is found by weighted verge n i w i i (7) n i w i where n is the number of lines nd wi is weight. The weight is the number of extrcted points for one line which bout corresponds to the length of the line. The slope of the lines orthogonl to the min direction of the building re given by orthogonl (8) Such vlues hve to be converted before the verging. The second step clcultes the c-vlues of equtions (5) nd (6) by lest squres djustment. These equtions re liner nd the djustment is, therefore, very simple. The unknowns (vector x) re then obtined by lest squres djustment using Ax b r (9) The mtrix (A) nd the vectors (x, b, r) hve the following designtions: k k k k k k k k 3 3 k k k k x rx y ry c x3 r x3 c y3 ry3 k = + c3 x4 rx4 k 3 c4 y4 ry4 k x rx k 3 y ry The mtrix elements k, k, nd k3 re clculted fter k () () x ( T T A A ) A b (4) 6..3 Clcultion of corner points The djusted coordintes of the corner points re clculted by p A x Eqution 3 cn be extended by weight mtrix (5) W dig( w, w,... wn ) (6) nd the unknowns (ci) re then derived by x ( T T A WA ) A Wb (7) The djustment by lest squres procedure cn lso derive ccurcy vlues. The estimted residuls re obtined by r p b (8) from which the vrince fctor nd the covrince mtrix for the corner coordintes re derived by T r W r (9) n u p T T A( A WA A () ) The ccurcy of the corner coordintes by mens of the covrince mtrix is n interior ccurcy only. For the ssessment of the exterior ccurcy we need ccurte reference vlues Closing of polygons The polygons hve to be closed. It is chieved by repeting the first point in the list to be used in plotting. 7. ASSESSMENT OF THE THEMATIC AND GEOMETRIC ACCURACY The ssessment of the ccurcies hs to be crried out seprtely for the results of the clssifiction, the enhnced mp of level, nd for the geometric ccurcy of the enhnced mp of level. 7. Assessment of the clssifiction The pplied ccurcy mesures re error mtrix, nd overll user s nd producer s ccurcy. The formuls nd definitions re given in (Conglton nd Green, 9).

6 7. Assessment of the enhnced mps The ssessment of the themtic ccurcy by the mentioned mesures cn lso be crried out for the enhnced mp of level. The ssessment of the level results my use n ccurcy mesure tht is bsed on objects. The number of objects in the scene re then compred with the detected nd mpped ones. 7.3 Assessment of the geometric ccurcy The ssessment of the geometric ccurcy is crried out by mens of the corner point coordintes. They re well-defined t DSM-bsed orthoimge The ccurcy mesures, Root Men Squre Error (RMSE) nd Men (µ), re clculted for ech of the buildings. The Men is the verge displcement of the enhnced mp with regrd to the reference. The comprison of the two dt sets requires tht n equl number of corner points exists. In relity this my not lwys be the cse. In (Avbelj et l., 5) metric is proposed tht evlutes the differences between polygons nd line segments. This so-clled PoLIS method clcultes orthogonl distnces of vertices to line segments. We prefer the RMSE/µ s mesures becuse of simplicity nd becuse they re used s stndrd in topogrphic mpping. 8. EXAMPLES OF CARTOGRAPHIC ENHANCEMENTS The pplied dt re prt of the ISPRS D semntic lbelling contest (ISPRS WG III/4, 4). The selected test site is city re in Germny where high buildings re close to ech other. Trees, bushes nd grss plnes re situted between the buildings. Mny crs re on rods nd prking lots. 8. Description of source dt The originl imgery is tken by photogrmmetric cmer (Zeiss DMC). The imges hve four bnds (RGB+NIR) nd re of very high sptil resolution (GSD=.9 m). The exposure occurred t sunshine which resulted in long shdows beside elevted objects. A digitl surfce model (DSM), normlized digitl surfce model (ndsm), flse-colour orthoimge (cf. Figure 5), nd reference mp were derived from the imges by the orgnizers of the test. The reference mp is mnully produced by mens of the DSMbsed orthoimge nd consists of five mjor urbn lnd cover clsses ( impervious surfces, building, low vegettion, tree, nd cr ). The selected test site covers 4 h. 8. Clssifiction The clssifiction strts with the trining of the clssifier. The chosen formul in modelling of the clsses uses five vribles: ref ~ ndsm + ndvi + sd_z_5 + b + sd_b_5 () where ref=reference clss, ndsm=normlized digitl surfce model (dz-vlue), ndvi=normlized difference vegettion index, sd_z_5=stndrd devition of the Z-vlue (elevtion), b=intensity vlue of the ner-infrred chnnel (bnd) of the true orthoimge, sd_b_5=stndrd devition of the intensities of bnd (b). The ndsm (dz) ttribute is the height bove ground nd is clculted by dz=dsm-dtm () The ndvi is derived from the intensities in the NIR-bnd nd the Red-bnd ccording to eqution (). ndvi =(I_NIR-I_R)/(I_NIR+I_R) () where I_NIR=intensity in the NIR-bnd, I_R= intensity in the R-bnd. The units of dz re meters (m) nd of I_NIR nd I_R digitl numbers (DN) in the rnge -55. The clcultion of the stndrd devitions of the Z-vlues (sd_z_5) nd of the infr-red bnd (sd_b_5) used the surrounding of 5 x 5 pixels of the digitl elevtion model nd of the spectrl bnd respectively. The Z-vlues re not used s ttributes due to the reltively big height differences of Z =38 m in the re of the test site. The decision tree (cf. Figure 6) is trined by mens of n djcent mp comprising 995 x 783 pixels (or 4.3 h) which contins ll six clsses. The clss clutter/bckground consists minly of wter (river) in this re. Figure 5. DSM-bsed ortho imge (flse-colour) Figure 6. Decision tree derived from n existing lnd cover mp. (b= building, s= impervious surfces, c= cr, m= clutter/bckground, v= low vegettion, t= tree ; sdz5=stndrd devition of elevtions in the 5x5 pixels surroundings [m], ndsm=normlized digitl surfce model [DN], ndvi=normlized difference vegettion index.

7 8.3 Results The derived ccurcy mesures re contined in Tble. The user s ccurcy revels tht the clsses impervious surfces nd building re bove 8%. The clsses low vegettion nd trees re less ccurte (59% nd 74% respectively). The clss cr is 6% nd clss clutter/bckground % only. It mens tht these two clsses could not be determined t ll. produces some res of no dt which hve to be filled gin. In this wy the crtogrphic qulity cn be improved. clss ucc pcc [%] [%] imp_surf 84 5 building low_veg tree cr 6 84 clutter Tble. User s ccurcy (ucc) nd producer s ccurcy (pcc) of the test site The overll ccurcy is clculted with 64.3% (95% CI: 64.3% %). The clculted confidence intervls (CIs) re very nrrow due to the big number of checkpoints (4.93 million points). The CI-vlues for the user s nd producer s ccurcy re therefore not given. In order to evlute the chieved ccurcy comprison with the results of the trining re re clculted (cf. Tble ). clss ucc pcc [%] [%] imp_surf building 9 73 low_veg tree cr 8 65 clutter 67 8 Tble. User s ccurcy (ucc) nd producer s ccurcy (pcc) of trining re. The user s ccurcy for the clss cr is lso poor (8%), the clss clutter/bckground, however, 67%. The overll ccurcy is 69.9%. It should be mentioned tht the clss cr nd clutter/bckground re not topogrphic objects nd hve, therefore, been removed in the crtogrphic enhncement. 8.4 Crtogrphic enhncement The result of the crtogrphic enhncement (level ) is depicted in Figure 7. It is crried out fter the proposed procedures described in Section 6. For the genertion of level qulity, the progrm pckge EBImge ws pplied (Pu, 3). The morphologicl opertions used structuring element of 5 x 5 pixels. When generting the outlines of buildings, the selected prmeters were x pixels (size of the moving window) nd. (thresholding offset from the verged vlue). The minimum re of building to be mpped ws ssumed to be 5 m nd for the res of clss low vegettion m. The res of clss impervious surfce used s threshold rdius of m nd the res of clss tree rdius of 4 m. All objects smller thn these thresholds were removed. This generliztion Figure 7. Enhnced lnd cover mp - level. (red= building, drk green= tree, green= low vegettion, gry= impervious surfce, white= no dt ) 8.5 Themtic ccurcy of the enhnced mp The themtic ccurcy of the topogrphic objects is contined in Tble 3. The use of other ttributes nd/or thresholds my improve the results. The process of enhncement cn be utomted s well. clss ucc pcc [%] [%] imp_surf 88 5 building 86 8 low_veg tree 8 8 Tble 3. User s ccurcy (ucc) nd producer s ccurcy (pcc) of enhnced lnd cover mp (level ) 8.6 Geometric ccurcy The coordinte errors clculted from eqution (9) re very smll (. pixel or.9 m). This is n interior ccurcy only. The root men squre errors, derived from reference vlues, re bsolute errors (cf. Tble 4). The verges of ll RMSE_x nd RMSE_y re. m nd.7 m respectively when the mnully derived mp (GT) ws used s reference. Reference vlues were lso derived by digitizing the corner points of buildings on top of the DSM-bsed orthoimge. The results re bout the sme. Altogether 3 corner points hve been checked. The verges of the stndrd devitions (σx, σy) re bout the sme s the RMSE vlues. This indictes tht the systemtic shifts (µx, µy) of the coordintes with regrd to the reference re very smll. The results my be improved when D trnsformtion is pplied. Besides the shifts rottion nd scle fctors will then be corrected too. Building 4 includes side the slope of which is close to 9. In such cse, the line prmeters θ nd ρ re clculted. This requires lineriztion of eqution (3) nd itertions when the pproximte vlues of the prmeters re not very ccurte. Furthermore, the clcultion of the corner points will then be bsed on eqution (3) s well.

8 Avbelj, J., Müller, R., Bmler, R., 5.A metric for polygon comprison nd building extrction evlution, IEEE Geosci Remote S, vol., no., 5 p. Breimn, L., Friedmn, J., Stone, C.J., Olshen, R.A., 984. Clssifiction nd regression trees. CRC Press. Conglton, R., G, Green, K., 8. Assessing the ccurcy of remotely sensed dt. CRC Press. El-Ashmwy, K. L. A., 6. Testing the positionl ccurcy of OpenStreetMp dt for mpping pplictions, Geodesy nd Crtogrphy, vol. 4, issue, pp Figure. Enhncement of clss building, level # of # of GT [m] Ortho [m] building corners RMSEx RMSEy RMSEx RMSEy verge Tble 5. Geometric ccurcy of the enhnced mp (level ). 9. DISCUSSION AND CONCLUSION The pplied method used pixels of the orthoimges s units. The themtic mp used for trining contined six clsses but ws not identicl with the re to be clssified. The distribution of the res of the produced lnd cover mp ws therefore different from the distribution in the trining re. The obtined user s ccurcy is pretty good for the clsses building (88%) nd impervious surfce (84%). The detection of crs is poor with the selected pproch. The qulity of the input dt nd of the reference dt is importnt for good results nd should, therefore, be tested. For exmple, the ccurcy of the elevtions (Z) is of lower ccurcy t the boundries of buildings. These res could hve been ignored in the ssessment. The obtinble ccurcies would then definitely be higher. The crtogrphic enhncement improves the qulity of the lnd cover mp. The themtic ccurcy is bout the sme for the clsses building, impervious surfces, nd low vegettion. The clss tree is much worse due to the threshold for minimum re. The pplied clssifier (DT) could esily hndle the reltively lrge mount of dt. The processing nd plotting of ll clsses in mp-like colours required high processing times. The geometric ccurcy derived from 3 corners of buildings were RMSE_x=. m nd RMSE_y=.7 m. According to the positionl ccurcy stndrds for digitl geosptil dt, e.g. in USA, n ccurcy of RMSE_x=RMSE_y=.m is required for mp scles in the rnge : to :4 (ASPRS, 5). REFERENCES ASPRS, 5. ASPRS Positionl Accurcy Stndrds for Digitl Geosptil Dt, Photogrmm Eng Rem S, 8, (3), pp. A A6. Awrngjep, M., Rvnbkhsb, M., Frser, C.,. Automtic detection of residentil buildings using LIDAR dt nd multispectrl imgery, ISPRS J Photogrmm 65 (5), Friedl, M.A., Brodley, C.E., 997. Decision tree clssifiction of lnd cover from remotely sensed dt. Remote Sens. Environ. 6, Gross, H., Thoennessen, U., 6. Extrction of lines from lser point clouds, In: Symposium of ISPRS Commission III: Photogrmmetric Computer Vision PCV6. Interntionl Archives of Photogrmmetry, Remote Sensing nd Sptil Informtion Sciences, pp Höhle, J., 4. Genertion of D lnd cover mps for urbn res using decision tree clssifiction, ISPRS Annls of the Photogrmmetry, Remote Sensing nd Sptil Informtion Sciences, vol. II-7, pp. 5-. ISPRS WG III/4, 4. ( June 6). Jin, R., Ksturi, R., Schunck, B.G., 995. Mchine vision, McGrw-Hill, Inc., ISBN Konecny, G.; Breitkopf, U.; Rdtke, A.; Lee, K., 5. The sttus of topogrphic mpping in the world - UNGGIM- ISPRS project -5, finl report, Leibniz Universität Hnnover, 64 pp. Li, H., Zhong, C., Hu, X., Xio, L., Hung, X., 3. New methodologies for precise building boundry extrction from LiDAR dt nd high resolution imge. Sensor Rev, 33/, pp Li, Y., Zhu, L., Shimmur, K., Tchibn, K.,. A refining method for building object ggregtion nd footprint modelling using multi-source dt. Interntionl Archives of the photogrmmetry, remote sensing nd sptil informtion sciences, vol. XXXIX-B3, pp Niemeyer, J., Rottensteiner, F., Soergel, U., 4. Contextul clssifiction of lidr dt nd building object detection in urbn res. ISPRS J Photogrmm 87, pp Pu, G., Sklyr, O, nd W. Huber, 3. Introduction to EBImge - n imge processing nd nlysis toolkit for R, ge.html (3 June 6) Smpth, A., Shn, J., 7. Building boundry trcing nd regulriztion from irborne LiDAR point clouds. Photogrmm Eng Remote S 73 (7), pp ACKNOWLEDGEMENT The uthor thnks the ISPRS WG III/4 for providing test dt.