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1 Provided by the author(s) ad Uiversity College Dubli ibrary i accordace with publisher policies., Please cite the published versio whe available. Title Evaluatio of Automatically Geerated 2D Footprits from Urba idar Data Authors(s) Truog-Hog, ih; aefer, Debra F. Publicatio date Coferece details ISPRS Geospatial Week 2015, a Grade Motte, Frace, 28 September - 3 October 2015 Publisher Iteratioal Society for Photogrammetry ad Remote Sesig (ISPRS) Item record/more iformatio Publisher's versio (DOI) /isprsarchives-X-3-W Dowaloaded T02:59:18Z The UCD commuity has made this article opely available. Please share how this access beefits you. Your story matters! (@ucd_oa) Some rights reserved. For more iformatio, please see the item record lik above.

2 EVAUATION OF AUTOMATICAY GENERATED 2D FOOTPRINTS FROM URBAN IDAR DATA. Truog-Hog a, *, D. aefer a a Urba Modellig Group, School of Civil, Structural ad Evirometal Egieerig ad Earth Istitute, Uiversity College Dubli, Belfield, Dubli, IE (lih.truoghog, debra.laefer)@ucd.ie Commissio III, WG III/5 KEY WORDS: idar data, Aerial aser Scaig, Buildig Detectio, Road Detectio, Evaluatio Strategy ABSTRACT: Over the last decade, several automatic approaches have bee proposed to extract ad recostruct 2D buildig footprits ad 2D road profiles from AS data, satellite images, ad/or aerial imagery. Sice these methods have to date bee applied to various data sets ad assessed through a variety of differet quality idicators ad groud truths, comparig the relative effectiveess of the techiques ad idetifyig their stregths ad short-comigs has ot bee possible i a systematic way. This ope cotest was desiged to overcomig this shortcomig. Specifically, participats were asked to submit 2D footprits (buildig outlies ad road profiles) derived from AS data from a highly dese data (approximately 225 poits/m 2 ) across a 1km 2 of cetral Dubli, Irelad. The proposed evaluatio strategies were desiged to measure ot oly the capacity of each method to detect ad recostruct 2D buildigs ad roads but also the quality of the recostructed buildig ad road models i terms of shape similarity ad positioal accuracy. The evaluated methods will represet those submitted as part of IQPC INTRODUCTION The availability of three-dimesioal (3D) poit clouds offers a potetial resource for wide rages of applicatios (e.g. evirometal plaig ad moitorig, computatioal simulatio, disaster maagemet, security, telecommuicatios, locatio-based services). Urba, two-dimesioal (2D) footprits, which primarily iclude 2D footprits of buildigs ad the road etwork, play importat roles i these applicatios ad ca be a major resource for geeratig fial 3D models. For example, aycock ad Day (aycock ad Day, 2003) geerated 3D buildig models by extrudig 2D buildig footprits with the buildig height derived from aerial laser scaig (AS) data. Furthermore, a part of the digital road map ca be subsequetly geerated based o a 2D road profile. A umber of researchers have addressed the problem of extractio ad recostructio of 2D buildig footprits ad 2D road profiles from AS data, satellite images, ad/or aerial imagery (Boyko ad Fukhouser, 2011; Clode et al., 2005; Kwak ad Habib, 2014; afarge ad Mallet, 2011; Zhag et al., 2006). Proposed methods have bee tested o differet data sets, ad the authors have also used various evaluatio criteria ad groud truth resources. For example, Boyko ad Fukhouser (Boyko ad Fukhouser, 2011) maually geerated groud truth of a road etwork ad proposed five comparative quatities (completeess, correctess, quality, average spill size ad prevailig spill directio) to evaluate extracted roads. This causes difficulty i geeratig a cosistet comparative assessmet of the methods. Thus, this cotest is called participats to submit resulted 2D footprits (buildig outliers ad road profiles) from AS data provided by the cotest orgaizers for evaluatio. The cotest also opes a challege i detectig ad recostructig road profiles from AS data oly because several curret methods required fusio data. The success of this cotest ca possibly provide useful iformatio for establishig strategies for automatic urba 2D footprits from AS data. The cotest uses a highly dese poit cloud (225 millio poits coverig approximately 1km 2 area) of Dubli, Irelad s city cetre. The data has Cartesia system coordiates ad itesity values ad was merged from 44 flight strips. The flight pla was desig to maximize data acquisitio o the buildig facades. The participats are asked to submit the results of automatically geerated 2D buildig footprits (Task A) ad/or 2D road profiles (Task B) from three pre-desigated sub-areas of the study area. The cotest orgaizers will evaluate submitted results based o the groud truth provided the Ordiate Survey Irelad (OSI) ad OpeStreetMap (OSM). The task descriptio, groud truth, ad evaluatio for each task are preseted i Sectio 3-5. Fially, a wier ad two ruers up for each task will be selected based o the overall evaluatio scores. 2. DATA DESCRIPTION The test area is approximately 1km 2 ad cosists of 205 blocks, each of which may cotai i excess of a doze buildigs per block, as show i Figure 1. The typical buildig is 11 15m i height, less tha 5m i width, ad 6 10m i legth (Clarke ad aefer, 2012). The buildigs are mostly closely spaced or abuttig each other, with some sharig a adjoiig wall, commoly referred to as a party wall. The dataset was acquired by AS usig the FI-MAP 2 system, which geerated 1000 pulses for each sca lie. The system operated at a sca agle of 60 degrees. The quoted accuracy of the FI-MAP 2 system is 8 cm i the horizotal plae ad 5cm i the vertical directio, icludig both laser rage ad avigatioal errors (Hiks, 2011). Acquired poits were provided i a global coordiate system with referece to the * Correspodig author

3 Natioal Irish Grid (Irish Grid), relatig to the use of a Global Navigatio Satellite System (GNSS) to determie the aircraft positio durig scaig. The FI-MAP 2 system is capable of recordig up to four echoes for each emitted pulse ad spectral data with itesity values. The domiat directios of the flight tracks were chose as orth-east, orth-west, south-east ad south-west. The flight attitude varied betwee ~ m (as low as possible with respect to approval by the Irish Aviatio Authority), with a average elevatio of ~400m. A total of 2,823 flight path poits were collected durig data acquisitio. As a result, a poit cloud was merged from 370,154 sca lies resultig i a typical desity of 225 poits/m 2. The echo distributio is show i Table 1. The vast majority of poits were first echoes. Secodary echoes costituted oly a small portio of the poits, as the overwhelmig majority of surfaces i the study area was formed of solid objects (i.e. streets ad buildigs). For further iformatio about this AS data, participats are referred to Hiks (2011). The data set was orgaized ito 9 tiles, each coverig 500m x 500m (Figure 1), which is 5.8 Gb i size ad stored with a AS format. The data set is ow publicly available. Echo Cout Percetage (%) 1 st 217,497, d 7,902, rd 383, th 4, Total 225,788, Table 1. Echo distributio of acquired AS poits Figure 2. Cotest Areas 3. TASK AND SUBMISSION Task A is to extract a poit cloud affiliated with buildigs ad to recostruct 2D buildig footprit from these poits. I urba areas, buildigs are ofte abuttig or sharig a adjoied wall. These buildigs also have similar morphology (i.e. height, width or similar roof cofiguratio), which poses a major challege for automatic algorithms i buildig detectio ad buildig boudary recostructio. Participats were asked to submit the results from their algorithms i two sets of file results: (1) ASCII files cotaiig AS data sets of each buildig, where each row represets the x-, y-, ad z- coordiates of the data poits ad (2) ASCII files cotaiig the buildig footprit describig as a polygo, where each row cotais both the x- ad y- coordiates of the polygo vertices. The file amig covetio should be Buildig_X1_Y1_X2_Y2, where the pairs X1ad Y1 ad X2 ad Y2 are two opposite corers of the boudig box of the dataset o a horizotal plae. Task B is to extract a poit cloud of the roads ad to recostruct the 2D road profiles icludig the pavemet edges. Similar to Task A, the participats were asked to submit two ASCII files cotaiig: (1) AS data poits of the road etwork ad (2) the polygos describig pavemet edges of the road etwork. Furthermore, the submitted files were to be amed aki to Task A usig the coordiates of two opposite corers of the boudig box of the road segmet. I the case that oly ASCII files cotaiig data poits of either buildigs or road etwork were submitted, the buildig footprits ad the pavemet edges of the road were to be geerated by the orgaizer s algorithm for further evaluatio. Figure 1. Acquired AS area i Dubli cetral ad AS tiles (cotest area outlied i red) Three subsets of the data are used for this competitio (Figure 2). Area 1 cotais sparse buildigs, a large gree area, ad trees. Area 2 has both buildig blocks ad buildigs sharig walls, as well as some trees. Area 3 cotais mostly low brick buildigs ad o trees. The data of each area (Area 1, 2 ad 3) were extracted from the origial data set ad is 1.1 Gb i size i a ASCII file (Zip file), where each row represets x-, y-, z-coordiates ad itesity of each data poit, or i a AS format (3.8 Gb). Either the data sets of the study area or these of the the cotest are ca be dowloaded through the webpage of IQmulus project. 4. GROUND TRUTH The groud truth cosisted of 2D footprits provided by the OSI. The 2D footprits primarily cotai 2D buildig boudaries ad road profiles (cetre ad edges). However, buildigs ad road etwork ca be chaged over period time, which may ot update i OSI 2D footprits. The buildig boudaries ad road cetres derived from OSM were to be a supplemet resource. The 2D footprits from OSI ad OSM are show i Figure 3. It ca be see clearly a majority of footpaths i Area 1 are ot available i OSI 2D footprit ad they ca derived from OSM data.

4 For the level of shape similarity ad positioal accuracy, the buildig footprit from ExB will be cosidered, if this buildig overlaps ay buildig from the GtB. To measure a shape similarity, the differeces i area ad perimeter of each buildig are computed, which are give i Eq. 4 ad 5. A GtBi A ExBi A (4) i1 GtBi ExBi (5) i1 where GtB ExB A GtBi = the areas of the buildig footprit from GtB A ExBi = the areas of the buildig footprit from ExB GtBi = the perimeter of the buildig footprit from ExBi = the perimeter of the buildig footprit from = the umber of the buildigs Fially, summig the absolute, mea, ad stadard deviatio of these differeces (area ad perimeter) are used to express the shape similarity. 5.1 Task A Figure 3. Groud truth from OSI ad OSM 5. EVAUATION STRATEGY The evaluatio process idetifies the level of locatioal deviatio, the level of shape similarity, ad the positioal accuracy of the extracted buildig footprit (ExB), with respect to the groud truth buildig (GtB). For the locatio deviatio, quality idicators ivolvig True Positive (TP), False Positive (FP) ad False Negative (FN) measure the overall extractio ad recostructio of the buildig footprits. These idicators ca be determied after mappig extracted results oto the GtB. If the object from the ExB matches oe from the GtB, it is TP. If the object from ExB does ot match to oe from the GtB, it is FP; otherwise, if the object from GtB does ot match oe from the ExB, it is FN. These quality idicators are illustrated i Figure 4. Completeess, correctess, ad quality will be the measured idicators of the submitted results, which are expressed i Eq. 1, 2 ad 3. Figure 4. Illustratio of determiig TP, FP, ad FN TP Comp (1) TP FN TP Corr (2) TP FP TP Quality TP FP FN (3) Furthermore, a positioal accuracy ca be described i terms of the accuracy ad cociseess of the buildig footprit, which is performed through establishig orietatio ad corer errors. The orietatio error () is the agle betwee ExBj (a side of the extracted buildig footprit i, ExBi) ad GtBj (a side of the groud truth buildig footprit i, GtBi), where GtBi is the closest side to the ExBj. For details of determiig a pair of GtBj ad ExBj, readers ca refer to Truog-Hog ad aefer (Truog-Hog ad aefer, 2015). I additio, the vertices /corers error (d) is defied as the Euclidea distace betwee the corers of the ExBi to its earest corer derived from the GtBi. The evaluated idicators are expressed as Eq.s 6 ad 7: m ExBj j j1 E oriet m (6) j1 ExBj E (7) corer d(pexbk,pgtbk ) k1 where ExBj = the side legth of ExBi j = the agle betwee the PREDi ad the GTi GTi = the side legth of GtBi m = the umber of boudary lies i the buildig footprit of iterest = the umber of the corers i ExBi I these error measuremets, ExBj was itroduced to avoid a heavy pealizatio for short, extracted, boudary lies (Okor et al., 2010). Subsequetly, average ad stadard deviatios are used to measure distributios of these quatities. 5.2 Task B Similar to Task A, the evaluatio process of Task B idetifies the level of locatioal deviatio ad the positioal accuracy of the extracted road profile (ExR), with respect to the groud truth road (GtR). Based o the miimum boudig box of GtR, a 2D grid with the cell size of 1m x 1m is geerated. Whe the GtR is mapped oto the 2D grid, the cell, C GtRi (x,y), has a value of 1, if ay pavemet edge or road surface of the GtR overlaps the cell

5 (gree cells i Figure 5); otherwise the value of C GtRi (x,y) is 0 (white cells i Figure 5). Furthermore, if the pavemet edge overlaps to C GtRi (x,y) = 1, the cell is divided ito two parts called iside road (C GtRIti (x,y)) ad outside road (C GtROuti (x,y), where C GtRIti (x,y) is a part of the cell havig the cetre drops betwee two pavemet edges of the road; otherwise it is C GtROuti (x,y). Notably, a total area of C GtRIti (x,y) ad C GtROuti (x,y) equals the cell area = 1m 2. This rule is also applied for ExR whe projectig ExR oto the 2D grid. Figure 6. Illustratio of computig TP, FP ad FN areas Figure 5. Classificatio of cells The locatioal deviatio, completeess, correctess, ad quality idicators metioed i Task A are measured, where these parameters ca be determied from Eq.1-3. True Positive (TP), False Positive (FP) ad False Negative (FN) are computed by comparig cell values from two 2D grids represeted by GtR ad ExR, as expressed i Eq.s 8-10 ad Figure 6. TP = C FP = C FN = C TP = 0 FP = 0 FN = C TP = 0 FP = C FN = 0 where GtRIti ExRIti GtRIti GtRIti (x, y) C (x, y)\ C (x, y)\ C ExRIti GtRIti ExRIti C If C (x, y) C (x, y) If C (x, y) C (x, y) If C (x, y) GtRi (x, y) 1 (x, y) 0 (x, y) 0 GtRi (x, y) 1 (x, y) 1 (8) (9) GtRi ExRIti (10) (x, y) 1 C GtRi = the cell value from GtR C = the cell value from ExR C GtRIti = the areas of a part of C GtRi iside GtR C ExRIti = the areas of a part of C iside ExR The positioal accuracy ca be determied through differeces i locatio ad orietatio of the road edges betwee the groud truth ad extracted roads. A distace ad agle betwee the road edges from the groud truth ad the extracted results are proposed to measure those differeces. A pair of road edges ( GtRi from GtR ad from ExR) is iitially extracted. For that, if C GtRi (x,y) = 1, a pavemet edge segmet from GtR overlappig C GtRi (x, y) is computed, which is called GtRi. The, if the agle betwee the GtRi ad a horizotal directio ( x = [1,0]) is less tha or equal to 45 degrees, a pavemet edge segmet of the ExR o the same vertical grid to the C GtRi (x, y) closest to the GtRi is the. Otherwise, the pavemet edge segmet of the ExR o the horizotal grid to the C GtRi (x, y) closest to the GtRi is the (Figure 7). Figure 7. Illustratio of determiig a pair of road edge segmets ( GtRi ad ) From the pair of the road edge segmets, the distace ad orietatio errors ( GtRi ad, respectively) ca be computed accordig to Eq.s 11 ad 12, where the distace betwee GtRi ad is the distace betwee the middle of the ad the GtRi. d( GtRi i1 E distr (11) i1 i1, i i1 E orietr ` (12) where = the umber of pairs of the road edge segmets j = the agle betwee the GtRi ad the Fially, the wiers of each task are selected based o the overall evaluatio of the output quality, where all evaluated quatities are weighted equally. 6. CONCUSION Automatic approaches have bee proposed to detect ad recostruct buildig ad road profiles from AS data. Previously, these methods were evaluated by usig differet data sets associated with various differet criteria ad groud truths. That precludes a rigorous compariso of the advatages ad disadvatages of each method. This paper presets the objectives of the track i the IQPC 2015 cotest related to automatic buildig ad road detectio ad recostructio. The cotest was ru o dataset cosistig of AS data captured over 1km 2 of the Dubli s city cetre with a typical data desity of 225 poits/m 2. The success of this cotest ca possibly provide useful iformatio for establishig strategies for automatic urba 2D footprits from AS data. ) A evaluatio strategy was proposed to bechmark the results i terms of the capacity of the submitted results i detectig ad recostructig buildig ad road outlies. The evaluatio

6 process idetifies the level of locatioal deviatio, the level of shape similarity, ad the positioal accuracy of the extracted buildig footprits ad road profiles, with respect to the groud truth buildig ad road. The cotest was lauched i March The test datasets remai available o the webpage of the track. Participats are welcome to submitted for future evaluatio. ACKNOWEDGEMENTS Data acquisitio was geerously supported by Sciece Foudatio Irelad through PICA program. This work was fuded by the Europea Uio through ERC program. The authors gratefully ackowledge this support. REFERENCES Boyko, A., Fukhouser, T., Extractig roads from dese poit clouds i large scale urba eviromet. ISPRS Joural of Photogrammetry ad Remote Sesig 66, S2-S12. Clarke, J., aefer, D.F., Geeratio of a Buildig Typology for Urba Tuellig Risk Assessmet, Bridge ad Cocrete Research i Irelad, Dubli, Irelad. Clode, S., Rottesteier, F., Kootsookos, P.J., Improvig city model determiatio by usig road detectio from lidar data, Joit Workshop of ISPRS ad the Germa Associatio for Patter Recogitio (DAGM),'Object Extractio for 3D City Models, Road Databases ad Traffic Moitorig-Cocepts, Algorithms, ad Evaluatio'(CMRT05). Hiks, T., Geometric Processig Techiques for Urba Aerial aser Sca Data. Uiversity College Dubli. Kwak, E., Habib, A., Automatic represetatio ad recostructio of DBM from idar data usig Recursive Miimum Boudig Rectagle. ISPRS Joural of Photogrammetry ad Remote Sesig 93, afarge, F., Mallet, C., Buildig large urba eviromets from ustructured poit data, Computer Visio (ICCV), 2011 IEEE Iteratioal Coferece o, pp aycock, R.G., Day, A.M., Automatically geeratig large urba eviromets based o the footprit data of buildigs, Proceedigs of the eighth ACM symposium o Solid modelig ad applicatios. ACM, Seattle, Washigto, USA, pp Okor, B., Xiog, X., Akici, B., Huber, D., Toward automated modelig of floor plas, Symposium o 3D Data Processig, Visualizatio ad Trasmissio, Paris, Frace p. 8. Truog-Hog,., aefer, D.F., Quatitative evaluatio strategies for urba 3D model geeratio from remote sesig data. Computers & Graphics. Zhag, K., Ya, J., Che, S.-C., Automatic costructio of buildig footprits from airbore IDAR data. Geosciece ad Remote Sesig, IEEE Trasactios o 44,