Jakarta, Indonesia

17 Pages·2017·11.31 MB·English

remote sensing
Article
Coupling Uncertainties with Accuracy Assessment in
Object-Based Slum Detections, Case Study:
Jakarta, Indonesia
JatiPratomo ID,MonikaKuffer* ID,JavierMartinez ID andDivyaniKohli
FacultyofGeo-InformationScience&EarthObservation(ITC),UniversityofTwente,7514AEEnschede,
TheNetherlands;[email protected](J.P.);[email protected](J.M.);[email protected](D.K.)
* Correspondence:[email protected];Tel.:[phone]
Received:27September2017;Accepted:10November2017;Published:13November2017
Abstract: Object-BasedImageAnalysis(OBIA)hasbeensuccessfullyusedtomapslums. Ingeneral,
the occurrence of uncertainties in producing geographic data is inevitable. However, most
studies concentrated solely on assessing the classification accuracy and neglecting the inherent
uncertainties. Our research analyses the impact of uncertainties in measuring the accuracy of
OBIA-based slum detection. We selected Jakarta as our case study area because of a national
policyofslumeradication,whichiscausingrapidchangesinslumareas. Ourresearchcomprises
of four parts: slum conceptualization, ruleset development, implementation, and accuracy and
uncertaintymeasurements. Existentialandextensionaluncertaintyarisewhenproducingreference
data. ThecomparisonofamanualexpertdelineationsofslumswithOBIAslumclassificationresults
intofourcombinations: TruePositive,FalsePositive,TrueNegativeandFalseNegative. However,
thehighertheTruePositive(whichleadtoabetteraccuracy),thelowerthecertaintyoftheresults.
Thisdemonstratestheimpactofextensionaluncertainties. Ourstudyalsodemonstratestheroleof
non-observableindicators(i.e., landtenure), toassistslumdetection, particularlyinareaswhere
uncertainties exist. In conclusion, uncertainties are increasing when aiming to achieve a higher
classificationaccuracybymatchingmanualdelineationandOBIAclassification.
Keywords: accuracy;uncertainties;object-based;slums;Jakarta
1. Introduction
ThemostrecentglobaltargetinslumreductionstatedintheSustainableDevelopmentGoals
(SDG)istoensureaccesstoadequate,safeandaffordablehousingandessentialservicesforallpeople
by2030[1]. Althoughthetargethasbeenstipulated,thenumberofslumdwellersisgrowing. In2012,
the number of dwellers living in urban slums was 863 million, which increased from 776 to 827
and 881 million in 2000, 2010 and 2015, respectively [2,3]. Highly dynamic changes in cities and
slums require techniques that can provide rapid and reliable information for policy formulations
related to slums. However, information regarding the growth and expansion of slums is sparsely
available[4]. Survey-baseddatacollectionmethodshavelimitationsduetolongtemporalgapsand
thedegreeofaggregation[5]. Thus,datamightbeobsoletewhenbeingused[6]. Meanwhile,although
satelliteimageryprovidesalmostreal-timeinformation[6],slumsandnon-slumsoftensharesimilar
surfacematerials[7],andslummorphologiesdifferwithinandacrosscities[8],whichmakestheir
identificationsomehowdifficult.
Among various approaches that were developed, Object-Based Image Analysis (OBIA) has
an excellent potential to extract slums using spectral as well as contextual information through a
hierarchicalprocedure[9]. However,oftentheclassificationprocessiscontextanddatadependent[6]
andnotflexibletobeappliedtoadifferentplace(city),differentimages(sensorsanddifferentdates).
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ThedevelopmentoftheGenericSlumOntology(GSO)aimedtobridgethisgap[6,9],byproviding
acompletecharacterizationofslumsusingmorphologicalindicators[7]atthreespatiallevels,i.e.,
environs,settlementandobject[10]. Thischaracterizationwasdevelopedbyadoptingthedurable
housingindicatorfromUN-Habitat[5].
AlthoughtheGSOassistsinslumdetection,itprovidesagenericconceptofslums[11],while
slumscanshowconsiderablediversitywithinacityandevenwithinasettlement[7,12]. Forinstance,
thesamecharacteristics(e.g., density)oftendifferlocallyanddependondevelopmentalstagesof
settlements[5]. Therefore,settlementshavingsimilardensitiesmightbeconsideredasslumsinone
place but as non-slums in another place [13]. This illustrates challenges faced when aiming at a
transferableslummappingapproachbasedonasetofgenericindicators.
Theabove-mentionedvariability(e.g.,spatial,temporal,sensors)requiresalocaladaptationof
theGSOfromexpert-domainknowledge. Onewaytodevelopsuchaknowledge-baseisbyusingan
ontology[14]. Theusageofanontologymaychangethenumericalapproach,whichiscommonlyused
inimageanalysis,toasymbolicapproachthatwillfittheexpert-domainknowledge[15].Theroleofthe
knowledge-basedapproachinimageclassificationhasbeenstressedbyseveralauthors. Forinstance,
bydevelopingontology-basedclassificationusingspectralrules[15],andknowledge-basedregion
labelling[14].
Thus, in the OBIA context, adaptations of such a ruleset for different images are
inevitable[7,16,17]. Nonetheless,itiscrucialtopromotetransparencyoftheadaptationstoensure
objectivity[16],inmeasuringtransferabilityoftheruleset[18]. Here,transferabilityisdefinedasthe
degreeofadaptationsofarulesettoproducecomparableresultsfromdifferentimagingconditions[7].
PreviousstudiesonOBIA-basedslumdetectionfocuseitheroncomparabilityoftheresults[7,17]or
onthedegreeofadaptations[19,20]andbothapproachesuseaccuracyasabenchmark.
Measuringtransferabilitybyonlyconsideringtheaccuracyindicatorsasabenchmarkhassome
shortcomings. First,theoccurrenceofuncertaintiesinproducinggeographicdataisinevitable[21],
andthelevelofuncertaintieswillpropagatethroughthewholeprocesschain[22]. Second,inOBIA,
manualimageinterpretationiscommonlyusedasreferencedata[23],oftenproducingambiguous
resultsassomeinterpretersdelineatemoredetailedobjectsandtheothersmaygeneraliseobjects[24].
Third,itishardtodefinetheexacttransitionbetweenslumsandnon-slums[25].Fourth,thedifferences
inexperienceandthewaytoconceptualiseslumsamonginterpretersmayleadtodifferentdelineations
ofreferencedata[25]. Hence,reflectingontheuncertaintiesmentionedabove,itiscrucialtoconsider
theseintheaccuracyassessmentforOBIAclassifications[24].
Inthispaper,weanalysetheimpactofuncertaintiesinproducingreferencedatafortheaccuracy
assessmentofOBIA-basedslumdetection.Weorganisedourstudyintofoursections.First,wedescribe
ourcasestudy. Second,wediscussmaterialsandmethods,whichincludesthedevelopmentofOBIA
rulesets,accuracyanduncertaintiesmeasurements. Third,wepresentourresults,fourth,wediscuss
theresultsandfifth,wepresenttheconclusionsofourresearch.
2. CaseStudy
Jakarta, the capital city of Indonesia, has grown enormously since a half-century ago, and its
metropolitan area is home to more than 30 million inhabitants [26]. The magnitude of economic
activitiesandthepresenceofnumeroussocietalinfrastructuresattractruralpeopletoJakarta.However,
thelackofcapacitiesbythelocalgovernmentinprovidingaffordablehousinghasforcedlow-income
householdstosettleinsubstandardhousingareas[27]. Thus,Jakartaisfacingchallengesintermsof
managingitsrapiddemographicandeconomicgrowth,whichalsoaffectsthegrowthofslums[28].
Approximately,60%ofJakarta’spopulation,predominatelyfromalow-incomehousehold,areliving
ininformalsettlementscalledkampungs.
Atthenationallevel,theGovernmentofIndonesiahassetthe100-0-100policy(100%accessto
cleanwater,0%slums,100%accesstosanitation)aspartoftheMediumTermNationalDevelopment
Program(RPJM)[29]. Thenationalgovernmentcommitted9.5billionUSDollarsfromthenational
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budgetuntil2019forthispurpose[30]. Hence,tomonitortheslumdynamicsiskeytodetermining
tdheetesrumccinesinsgo fthime psluecmceesnst ionfg imthpislepmoleincytin[3g1 ]t.hFiso rptohliicsyp u[3r1p]o. sFeo,rr etlhiaisb lpeuarnpdosuep, drealtieadblien faonrdm autpiodnatoend
sinlufomrsmiastrieoqnu oirne dsl.ums is required.
IInn ggeenneerraall,, ttoo ddeefifinnee sslluumm bboouunnddaarriieess iinn JJaakkaarrttaa iiss nnoott ssttrraaiigghhttffoorrwwaarrdd.. IInnffoorrmmaall ddeevveellooppmmeennttss
iinn JJaakkaarrttaa ssttaarrtteedd aa hhaallff--cceennttuurryy aaggoo wwhheenn JJaakkaarrttaa eexxppeerriieenncceedd rraappiidd uurrbbaanniissaattiioonn [[3322]].. AAtt tthhaatt ttiimmee,,
tthhee ppllaannnniinngg iinnssttiittuuttiioonnss wweerree nnoott eessttaabblliisshheedd [[3333]].. LLooccaallllyy,, tthheessee iinnffoorrmmaall sseettttlleemmeennttss aarree ccaalllleedd
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ggrroouuppss.. AAss aa rreessuulltt ooff tthhee cciittyy ggrroowwtthh,, kkaammppuunnggss eexxppaannddeedd aanndd bbeeccaammee mmoorree hheetteerrooggeenneeoouuss,, aallssoo
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hkaamvepubnegens hparvoev ibdeeedn wpritohvbidaesdic wfaictihli btiaessi,ca fnadcimlitaiensy, aonfdit smdawnyel olefr istsh davweellelegrasl hriagvhet sleognalt hriegirhtlas nodns tahnedir
plarnodpse ratnieds p[3r2o]p.eIrntieresm [3o2t]e. lyIns reenmseodteilmy asgenersye,di timisadgieffiryc,u iltt itso dmifafikceulat tdoi smtinacktei oan dbisettiwnceteinons lbuemtwaenedn
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ffoorrmmuullaattee aa sslluumm ddeefifinniittiioonn.. FFoorr iinnssttaannccee,, tthhee NNaattiioonnaall BBooaarrdd ooff SSttaattiissttiiccss ddeevveellooppeedd iinnddiiccaattoorrss
aaccccoorrddiinngg totot htehheo uhsoiunsginqgu aqliutyalaintyd manedn timoneendtitohnaetds luthmatb usilludmin gbcuainldbiengch caaranc tberei zcehdabraycitneardizeeqdu abtey
liinvaidngeqsupaatec eli[v3i7n]g. Mspeaacne w[3h7il]e. ,MtheeanMwinhiilsetr, ythoef PMuibnliisctrWy oorfk sPudbevliec loWpeodrkisn ddiecvaetolorpseadc cionrddiicnagtotros
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Ifnacteilrintiaetsi o[n38a]l.l yI,nttheernmaotisotncaollmy,m tohne lymeomst pcloomyemdodnelfiy neimtiopnlooyfeads dluemfiniistiboans eodf oan stluhmed ius rbabasleedh oouns itnhge
idnudriacbatloe rhs,owushienrge iansdliucmatoisrsa, nwahreearet haa stliusmch iasr aacnt earriseead thbayt laisc kchoafraacccteesrsisteods bafye lwacakte orfa ancdcessasn ittoa tsioafne,
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FFoorr ththeep uprupropsoesoef othf isthstius dsyt,uwdye,s ewleec tseedleacstuedb seat asurobusnetd aTreobuentddi sTtreibcte(ts idziesdtr2ic9t sq(suizaered k2il9o msqeutraerse)
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shluubm, sa.nSde cslounmd,s.t hSeecConildi,w thuen gCirliivweurnthga rtiviserlo tchaaltl yisa lsoscoaclilayt eadsswociitahtesdlu wmisthfl oslwusmtsh frloouwgsh tthhriosudgihst rthicits.
Tdhisitrrdi,ctt.h eTdhiirsdtr,i ctthheo udsisetsrivcat rihoouussteysp evsaorifosulus mtysp(ee.sg .,osfl usmlusmths a(tea.gre., loscluatmeds otnhatht earriev elrobcaantekd,n oeanr tthhee
rraivilerrobaadn,kn,e naeratrh ethCe ernatirlraolaBdu, snineaesr sthDei sCtreincttr(aCl BBDu)s.iness District (CBD).
(a) (b)
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aanndd WWesetstJ avJaavPar oPvrionvcien(cteh e(tmhee trmopeotrloitpaonliatraena ianrcelau diensclsuodmees psaormtseo fptahretsse opfr otvhienscee sp),raorveianbceosu),n daarreya
sboouurnced:aOryp esnosutrreceet: MOappen(2s0tr1e5e)t( OMpaepn S(t2re0e1t5M) a(pMFaonuunfadcattuiorner,,S uCtittoyn, CUoSl dSfitealtde, UabKb)r;e(bv.), seCloecutnedtrysu);b s(bet)
lsoeclaecteteddi nsuTbesbeett ldoicsatrtiecdt, iJna kTaerbtae.t Idmisatgreicst,o Juarkcaer:tGa.o IomglaegEe asrothur(c2e0:1 G5)o(oMgloeu Enataritnh V(2ie0w15,)C (AC,itUy,S AU)S. State
abbrev., Country).
RemoteSens.2017,9,1164 4of17
Remote Sens. 2017, 9, 1164 4 of 17
33.. MMaatteerriiaallss aanndd MMeetthhooddss
OOuurr rreesseeaarrcchh mmeetthhooddss aarree ccoommpprriisseedd ooff ffoouurr mmaaiinn ppaarrttss:: ((ii)) sslluummss ccoonncceeppttuuaalliissaattiioonn,, ((iiii)) OOBBIIAA
rruulleesseett ddeevveellooppmmeenntt,, ((iiiiii)) rruulleesseett iimmpplleemmeennttaattiioonn,, ((iivv)) aaccccuurraaccyy aanndd uunncceerrttaaiinnttyy mmeeaassuurreemmeenntt.. OOuurr
mmeetthhooddoollooggyy iiss sshhoowwnn iinn FFiigguurree 22,, aanndd tthhee ddeettaaiilleedd pprroocceessss iiss ddeessccrriibbeedd iinn tthhee ffoolllloowwiinngg ppaarraaggrraapphh..

FFiigguurree 22.. RReesseeaarrcchh mmeetthhooddoollooggyy ccoommpprriisseedd ooff ffoouurr mmaaiinn ppaarrttss aanndd tthheeiirr ffoolllloowwiinngg aaccttiivviittiieess..
In the first part, we related the definitions of slums by the local experts with image-based
In the first part, we related the definitions of slums by the local experts with image-based
information by using several observable visual elements, e.g., tone, shape, size, texture and
information by using several observable visual elements, e.g., tone, shape, size, texture and
association [6,10]. We selected five local experts from different backgrounds, i.e., government,
association [6,10]. We selected five local experts from different backgrounds, i.e., government,
consultants and NGOs. As mentioned in [25], the selected experts needed to have a professional
consultants and NGOs. As mentioned in [25], the selected experts needed to have a professional
knowledge of slums. Therefore, we selected experts that have been involved in programs related to
knowledgeofslums. Therefore,weselectedexpertsthathavebeeninvolvedinprogramsrelatedto
slums in Jakarta. From the government, we have interviewed two experts, one from the National
slumsin Jakarta. Fromthegovernment, wehave interviewedtwoexperts, onefromtheNational
Government (Ministry of Public Works), and one from the Local Government (Department of
Government(MinistryofPublicWorks),andonefromtheLocalGovernment(DepartmentofSpatial
Spatial Planning, Jakarta). In addition, we interviewed two experts from consultancies that were
Planning,Jakarta). Inaddition,weinterviewedtwoexpertsfromconsultanciesthatwereinvolvedin
involved in formulating the national policy of slums in Indonesia. Lastly, we interviewed one
formulatingthenationalpolicyofslumsinIndonesia. Lastly,weinterviewedonerepresentativefrom
representative from an NGO, who participated in monitoring settlement targets for the Millennium
anNGO,whoparticipatedinmonitoringsettlementtargetsfortheMillenniumDevelopmentGoals
Development Goals (MDG). Besides expert interviews, field observations were conducted in the
(MDG).Besidesexpertinterviews,fieldobservationswereconductedintheareasexpertsdelineated
areas experts delineated as slums. The characteristics of slums obtained during the interviews were
asslums. Thecharacteristicsofslumsobtainedduringtheinterviewswereusedfordevelopingthe
used for developing the ruleset for the OBIA-based slum detection.
rulesetfortheOBIA-basedslumdetection.
In the second part, we developed the OBIA-based ruleset for slum detection according to the
Inthesecondpart, wedevelopedtheOBIA-basedrulesetforslumdetectionaccordingtothe
definitions mentioned in the first step. In general, OBIA aiming to relate geographic features with
definitionsmentionedinthefirststep.Ingeneral,OBIAaimingtorelategeographicfeatureswithimage
image objects can be divided into two main parts, namely segmentation and classification [40]. In
objectscanbedividedintotwomainparts,namelysegmentationandclassification[40]. Ingeneral,
general, segmentation delineates regions (segments) of an image that share common attributes [41].
segmentationdelineatesregions(segments)ofanimagethatsharecommonattributes[41]. Theresult
The result is a relatively homogeneous and significant grouping of pixels [42]. Meanwhile, the
is a relatively homogeneous and significant grouping of pixels [42]. Meanwhile, the classification
classification process assigns each segment to a particular class according to predefined
processassignseachsegmenttoaparticularclassaccordingtopredefinedcharacteristics,e.g.,tone,
characteristics, e.g., tone, shape, size, texture and association. For segmentation, we used
shape,size,textureandassociation. Forsegmentation,weusedmulti-resolutionsegmentation(MRS)
multi-resolution segmentation (MRS) since this algorithm has been widely used in OBIA-based slum
sincethisalgorithmhasbeenwidelyusedinOBIA-basedslumdetectionstudies(e.g.,[5,12]).However,
detection studies (e.g., [5,12]). However, the implementation of MRS is dependent on the Scale
theimplementationofMRSisdependentontheScaleParameter(SP)[43],controllingtheheterogeneity
Parameter (SP) [43], controlling the heterogeneity of image objects [44]. The SP value is often selected
ofimageobjects[44]. TheSPvalueisoftenselectedinatrial-and-errorprocess[45]. Therefore,we
in a trial-and-error process [45]. Therefore, we employed the Estimation Scale Parameter (ESP) tool
employedtheEstimationScaleParameter(ESP)tool[43]todeterminethemostappropriateSP.
[43] to determine the most appropriate SP.
Inthethirdpart,weimplementedtherulesetinourstudyarea. WeselectedPleiadesimagery
In the third part, we implemented the ruleset in our study area. We selected Pleiades imagery
grantedfromtheEuropeanSpaceAgency(ESA)withstandard-orthobundlesfortheyearof2015,
granted from the European Space Agency (ESA) with standard-ortho bundles for the year of 2015,
withaspatialresolutionof0.5-mforR-G-B-NIRbands. Wemanagedtoobtainanimagewithacloud
with a spatial resolution of 0.5-m for R-G-B-NIR bands. We managed to obtain an image with a
coveroflessthan10%. Wepurposivelyselectedtwosmalltestareas(sizedkm2)),withoutanycloud
cloud cover of less than 10%. We purposively selected two small test areas (sized 1 km2)), without
cover. Forthefirsttestarea,weselectedanareawitharelativelysimilaragreementofslumboundaries
any cloud cover. For the first test area, we selected an area with a relatively similar agreement of
amongexperts,while,inthesecondarea,expertsconsiderablydisagreedaboutslumboundaries.
slum boundaries among experts, while, in the second area, experts considerably disagreed about
Lastly,inthefourthpart,wemeasuredtheaccuracyoftheclassificationresult.Manualdelineation
slum boundaries.
ofslumboundaries(ontopoftheimage)bylocalexpertswereusedtoproducethereferencedata,
Lastly, in the fourth part, we measured the accuracy of the classification result. Manual
asdemonstratedin[24,25]. Thus,wecomparedtheextractedslumsfromtheOBIAruleset,withthe
delineation of slum boundaries (on top of the image) by local experts were used to produce the
referencedatafromthelocalexperts. Thiscomparisonobtainedfourpossibleresults(Figure3),i.e.,
reference data, as demonstrated in [24,25]. Thus, we compared the extracted slums from the OBIA
truepositive(TP),falsepositive(FP),truenegative(TN)andfalsenegative(FN).
ruleset, with the reference data from the local experts. This comparison obtained four possible
results (Figure 3), i.e., true positive (TP), false positive (FP), true negative (TN) and false negative
(FN).
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Figure 3. Four possible results from combining classification result with the reference data produced
Figure3.Fourpossibleresultsfromcombiningclassificationresultwiththereferencedataproduced
by the experts.
bytheexperts.
We used three indicators for measuring accuracy, i.e., precision, recall and accuracy. Precision
Weusedthreeindicatorsformeasuringaccuracy,i.e.,precision,recallandaccuracy. Precision
or confidence describe the proportion of predictive-positive cases, which show a correct match with
orconfidencedescribetheproportionofpredictive-positivecases,whichshowacorrectmatchwith
the reference data [46]. It can be measured by comparing TP with TP and FP (1). Meanwhile, recall or
thereferencedata[46]. ItcanbemeasuredbycomparingTPwithTPandFP(1). Meanwhile,recall
sensitivity indicates the proportion of real positive cases that were correctly predicted. It can be
orsensitivityindicatestheproportionofrealpositivecasesthatwerecorrectlypredicted. Itcanbe
measured by comparing the number of TP, with TP and FN (2). Lastly, accuracy indicates the total
measuredbycomparingthenumberofTP,withTPandFN(2). Lastly,accuracyindicatesthetotal
correct positive and negative cases (i.e., TP and TN) to the total number of possible cases (i.e., TP, FP,
correctpositiveandnegativecases(i.e.,TPandTN)tothetotalnumberofpossiblecases(i.e.,TP,FP,
FN, TN) (3) [46]. Therefore, precision, recall and accuracy were calculated as:
FN,TN)(3)[46]. Therefore,precision,recallandaccuracywerecalculatedas:
TP
Precision= , (1)
TPTP+FP
Precision= (1)
TP+FP
TP
Recall= , (2)
TPTP+FN
Recall= (2)
TP+FN
TP+TN
Accuracy= TP+TN . (3)
Accuracy= TP+FP+FN+TN (3)
TP+FP+FN+TN
Regarding uncertainties, as pointed out in [25], the difficulties to draw exact boundaries where
Regardinguncertainties,aspointedoutin[25],thedifficultiestodrawexactboundarieswhere
slums change into non-slums and vice versa leading to uncertainty, i.e., existential and extensional
slumschangeintonon-slumsandviceversaleadingtouncertainty,i.e.,existentialandextensional
uncertainty [47]. First, existential uncertainty indicates the degree of confidence whether a slum
uncertainty[47]. First,existentialuncertaintyindicatesthedegreeofconfidencewhetheraslumexists
exists in reality [25,47], and it may depend on experts’ experience or conceptual difference upon
in reality [25,47], and it may depend on experts’ experience or conceptual difference upon image
image interpretations [25]. Second, extensional uncertainty indicates the area delineated as a slum
interpretations [25]. Second, extensional uncertainty indicates the area delineated as a slum with
with limited certainty [25].
limitedcertainty[25].
Furthermore, uncertainties also arose from different slum conceptualizations by local experts.
Furthermore,uncertaintiesalsoarosefromdifferentslumconceptualizationsbylocalexperts.
While [25] aimed to study the deviations of slum boundaries observed from Very High Resolution
While[25]aimedtostudythedeviationsofslumboundariesobservedfromVeryHighResolution
(VHR) images, our research emphasises the impact of various degrees of slum boundaries’
(VHR)images,ourresearchemphasisestheimpactofvariousdegreesofslumboundaries’agreements
agreements on the values of the accuracy assessment. To do so, we compared the classification result
onthevaluesoftheaccuracyassessment. Todoso,wecomparedtheclassificationresult(OBIAslum
(OBIA slum map for each test area) obtained in the third part with the reference data showing
mapforeachtestarea)obtainedinthethirdpartwiththereferencedatashowingvariousagreement
various agreement levels. For instance, first, we compared the classification result with an area
levels. Forinstance,first,wecomparedtheclassificationresultwithanareawherethereferencedata
where the reference data showed the highest agreement (all five experts agreed that an area is a
showedthehighestagreement(allfiveexpertsagreedthatanareaisaslum). Next, wemeasured
slum). Next, we measured the accuracy according to the indicators mentioned in (1) to (3). We
theaccuracyaccordingtotheindicatorsmentionedin(1)to(3). Werepeatedthisprocedureforeach
repeated this procedure for each subset and every degree of agreement (ranging from 1 to 5 experts).
subsetandeverydegreeofagreement(rangingfrom1to5experts). Thiscomparisonallowedusto
This comparison allowed us to examine the impact of different agreements in the reference data on
examinetheimpactofdifferentagreementsinthereferencedataonaccuracylevelsformappingslums
accuracy levels for mapping slums in Jakarta.
inJakarta.
RemoteSens.2017,9,1164 6of17
4. Results
4.1. SlumsConceptualisation
The result of the expert interviews shows the local diversity of slum characteristics (Table 1).
Theexpertfromthenationalinstitutions(i.e.,MinistryofPublicWorks)definedslumsaccordingto
thebuildingsize,which,ingeneral,issmallerinsizecomparedtonon-slumbuildings. Inaddition,
slumsarelocatedcommonlyontheriverbankornearrailroads,withirregularbuildingorientations.
The expert from the local government mentioned similar characteristics regarding the location
on the riverbank and near railroads. With regards to the difficulties to distinguish slum and
non-slum kampungs, the tenure status was often mentioned as a characteristic that could be used
for distinguishing. Experts (NGO and twoconsultants) also cameup with the slumcharacteristic
of small building sizes. In addition, they also mentioned that slums have irregular building
orientations,poorroofmaterialsandarelocatedontheriverbankandnearrailroads. Thelastexpert
(thesecondconsultant),however,onlymentionedbuildingsizeandirregularbuildingorientationas
slumscharacteristics.
Table1.Differentcharacteristicsanddefinitionsofslumsamonglocalexperts.(1)isfromthecentral
government;(2)isfromthelocalgovernment;(3)isfromNon-GovernmentOrganization(NGO),and
(4)and(5)arehousingpoliciesconsultants.
LocalExpert
Characteristics
(1) (2) (3) (4) (5)
√ √ √ √
1 Locatedon/closetheriverbank/railroad
√ √ √ √
2 Smallbuildingsize
√ √ √ √
3 Irregularbuildingorientation
√ √ √
4 Poorroofmaterial
√
5 Builtonillegalland
Accordingtothevisualimageinterpretations,localexpertshavedifferentagreementsonslum
locationsinourstudyarea. InFigure4a,weshowthedifferentagreementsofslumextents(delineated
byexperts),wheretheredareaandblueareasindicatethehighestandlowestagreementrespectively.
To give a better understanding regarding slum characteristics on the ground, we conducted field
observations. Forthefirstsample(Figure4b),weselectedanareaalongtheTebetTimurStreet,which
wasdigitizedbyfourofourexperts. Fromfieldobservations,thisareaischaracterisedbyitsproximity
to the river and has irregular building orientations. We also found that buildings in this area are
madeupofpoormaterials(e.g.,cardboard,plastics,corrugatediron,wovenbamboo). Inaddition,we
noticeddifferenttypesofroofmaterials(i.e.,rangingfromtilestocorrugatedirons). Forthesecond
example, we selected an area in Manggarai I street (Figure 4c), which shows diversity in terms of
expertagreementsonslums(rangingfromonetofiveexperts).
RemoteSens.2017,9,1164 7of17
Remote Sens. 2017, 9, 1164 7 of 17
(a) (b)
(c) (d)
Figure 4. Slums extracted from manual delineation by different experts. (a) shows the different
Figure 4. Slums extracted from manual delineation by different experts. (a) shows the different
agreements of slum extents, where the red colour indicates areas with the highest agreement and the
agreementsofslumextents,wheretheredcolourindicatesareaswiththehighestagreementandthe
blue colour indicates the lowest; (b) shows the ground conditions of slums where four experts
bluecolourindicatesthelowest;(b)showsthegroundconditionsofslumswherefourexpertsagreed;
agreed; (c) shows the ground conditions of slums, which were indicated as a slum by all experts; (d)
(c)showsthegroundconditionsofslums,whichwereindicatedasaslumbyallexperts;(d)showsthe
shows the ground conditions of a slum that was selected by one and two experts. The red boxes in (a)
groundconditionsofaslumthatwasselectedbyoneandtwoexperts.Theredboxesin(a)indicate
indicate our test areas.
ourtestareas.
4.2. OBIA Ruleset Development
4.2. OBIARulesetDevelopment
When developing the OBIA ruleset, we translated the characteristics of slums obtained from the
locWalh eexnpedretsv,e ilnotpoi nchgatrhaectOerBisItAicsr uthleast ecta,nw bee trreacnosglantieseddt hbey cah caormacptuerteisrt.i Tcshoe fasslsuomciastoiobnt aminaeyd infrcolumdeth e
loctaolneex, psehratps,e,i nstiozec, hteaxratucrteer aisntdic sastshoactiactainonbse. Treacboleg n2i ssehdowbys tahceo fmivpe ucthear.raTchteeriasstsicosc ioaft isolunmms atyhaint calrued e
tonues,esdh atop ed,esviezleo,pt etxhteu rruelaensedt. a ssociations. Table2showsthefivecharacteristicsofslumsthatareused
todeveloptheruleset.
FoFrotrh ethfie rfsitrscth cahraarcatcetreirsitsitci,c,s slulummss aarree ccoommmmoonnllyy llooccaatteedd oonn ththee rirviverebrabnakn koro nrenaera trheth reairlaroilardo.a d.
ThTuhs,uws, ewee memplpoloyyeedda a vveeccttoorr llaayyeerr oof frirvievresr asnadn rdairlraoilardo a(Odp(eOnpsterneestt rMeeatpM daatpa)d uastian)g upsrionxgimpirtoyx aism ai ty
rule. For the second and third characteristics, we associate the size and shape of the building with
asarule. Forthesecondandthirdcharacteristics, weassociatethesizeandshapeofthebuilding
RemoteSens.2017,9,1164 8of17
Remote Sens. 2017, 9, 1164 8 of 17
witthhet hsheasphea paneda nsidzes iozfe tohfe tsheegmseegnmt. eMnte.anMwehainlew, hfoirle t,hfeo rfotuhrethfo cuhratrhaccthearirsaticct,e wriset iacs,swoceiaates stohcei artoeotfh e
roomfamteartiearl iaolf osflusmlu mbubiuldiilndgins gwsiwthi ththteh etotnoen/ceo/lcooulro uorf othfet hseegsemgemnte.n Tt.hTe hleaslta scthcahraacrtaecrtiestriics tiisc imsomsto st
intienrteesrteisntgin.gU. Unlniklieket htehef ofouurrp prerevvioiouuss cchhaarraacctteerriissttiiccss,, tthhee llaasstt oonnee isis nnoot tddiriercetcltyl yobosbesrevravbaleb lferofmro mana n
imaimgea.gTe.h eTrheeforerfeo,rwe,e wusee dusaedp roax pyrionxdyi ciantdorictaotodre tteor mdeinteermthientee nthuer etsetnautures .sAtactcuosr.d Aincgcotordtihneg intote rtvhiee w
witihnttehrveieexwp ewrtitfhr otmhet heexpJaekrta rftraompr otvhien cJea,kJaarktaa rptaroivsiinmcep, leJmakeanrttain gis aimstrpilcetmzeonntiinngg rae gsutrlaictti ozno,nwinhgi ch
meraengsuliattiisonil,l weghailchto mceoannsst ritu icst iwlleigthali ntop croontsetcrtuecdt wzoitnheisn. pTrhotuesc,tewde zodneecsid. Tedhutso, wusee dtehceidzeodn tion gusme tahpe to
zoning map to delineate the protected zones, where any construction is illegal and has no legal
delineatetheprotectedzones,whereanyconstructionisillegalandhasnolegaltenurestatus.
tenure status.
The idea of using a non-observable indicator has induced us to develop two scenarios when
The idea of using a non-observable indicator has induced us to develop two scenarios when
implementingourruleset. First,werunourrulesetwithfourindicators(onlyobservable;indicator
implementing our ruleset. First, we run our ruleset with four indicators (only observable; indicator
number1to4inTable2). Second,weincludethenon-observableindicator(number5inTable2). We
number 1 to 4 in Table 2). Second, we include the non-observable indicator (number 5 in Table 2). We
appliedbothscenariosforthetwotestareas.
applied both scenarios for the two test areas.
Table2.Translationoftherealworldcharacteristicsintoimagedomaincharacteristicsinthecontextof
Table 2. Translation of the real world characteristics into image domain characteristics in the context
theGenericandtheLocalOntologyofSlums.
of the Generic and the Local Ontology of Slums.
RReeaall WWoorrllddD Doommaianin ImIamgea gDeoDmoaminain
11 LocLaotecdat eodn othnet hreivreivrberabnakn/kn/enaera rrarialriloroadad AssAocsisaotciioanti:o Dn:isDtaisntacnec teot RoiRvievre/rR/aRialriloraodad
22 SmSamll ablulbiludiilndgin sgizsiez e SizSei:z Sem:Samlla ll
33 IrreIgrruelgaur lbarubiludiilndgin ogroierinetnattaitoionn ShaSphea:p ceo:mcopmapcatnctensess s
4 PoorRoofmaterial Tone:Asbestos,corrugatediron
4 Poor Roof material Tone: Asbestos, corrugated iron
5 Builtintheillegalland Ancillarydata:LandUsePlan
5 Built in the illegal land Ancillary data: Land Use Plan
AfAtefrtewr ewaes assoscoiacitaetee aecahchs lsulummc chhaarraacctteerriissttiicc wwiitthh iittss ccoonnsseeccuuttivivee imimaagge eddomomaiani,n w,we deedveevloeplo opuor ur
rulreusleetseint iTnr Timribmleb’lse’esC eoCgongintiitoionn,,D Deevveeloloppeerr vveerrssiioonn 99 ((TTrriimmbbllee GGeerrmmaannyy GGmmbHbH, M,Munuinchic, hG,eGrmeramnya)n. y).
OuOrurru rluesleestetc acnanb bee ddiivviiddeedd iinnttoo twtwoo stsetpesp (sFi(gFuigreu r5e)—5f)i—rstfi, rbsatc,kbgarockugnrdo ruenmdovreaml, aonvda,l ,seacnondd,, sselucomn d,
sludmetedcetitoenc.t iIonn .theI nbatchkegrboaucnkdg rroeumnodvarle smteopv, awles tiempp,lewmeenimt MplRemS ewnitthM aR loSww SitPh (SaPl o=w 1) StPo e(xStPra=ct 1)
tobeaxctkragcrotubnadc kcglraossuens,d ic.ela., ssveesg,eit.aet.i,onv,e greatilartoiaodns,, rraoilardosa, dasn,dro athdes ,raivnedrst. hNeerxivt,e rws.e Napexptly, wae caopaprsley a
coasresgemseengtmateionnta ftoior nthfeo rretmheairneimnga iunninclgasusnifcielads ssiefigemdesnetgs,m anendt hs,earen dwhe eimrepwleemiemnpt loeumr ernutleosuetr frourl eslsuemtf or
sludmetdecettieocnt.i on.

Figure 5. Object Based Image Analysis (OBIA) ruleset flowchart, which starts with background
Figure5.ObjectBasedImageAnalysis(OBIA)rulesetflowchart,whichstartswithbackgroundremoval,
removal, followed by slum detection.
followedbyslumdetection.
In the first step, we find that, among various possible associations (i.e., tone, shape, size, texture
Inthefirststep,wefindthat,amongvariouspossibleassociations(i.e.,tone,shape,size,texture
and associations), which can be used for classification, the Normalized Difference Vegetation Index
andassociations),whichcanbeusedforclassification,theNormalizedDifferenceVegetationIndex
(NDVI: proportion between near-infrared and red band) shows its ability to detect the vegetation
(NDVI: proportion between near-infrared and red band) shows its ability to detect the vegetation
well. Each object that has an average value of NDVI greater than zero is classified as vegetation.
weHll.owEeavcehr,o ibf jwecet cthhoaotshe aas caonarasev eseraggmeenvtaaltuioeno, fveNgDetVatIiognr eisa tuenrdtehra-snegzmereontiesdc l(aFsigsiufireed 6)a. sHveengcee,t awtieo n.
Hoawree vinetre,niftiwoneacllhyo oovseera-sceogamresentsienggm beencatuatsieo nw,ev eagimet atoti oonbtiasinu nthdee rs-hseagpme eanndte dsiz(Fe igofu rthee6 )v.eHgeetnactieo,nw e
areclianstse natsi oclnoaslel yaso pvoers-ssibeglem toe nittsi nrgeabl eschaaupsee awnde saiizme. toobtaintheshapeandsizeofthevegetationclass
as closeaspossibletoitsrealshapeandsize.
RemoteSens.2017,9,1164 9of17
Remote Sens. 2017, 9, 1164 9 of 17

(a) (b) (c)
Figure 6. Impact of segmentation scale on vegetation classification. (a) shows segments with an
Figure 6. Impact of segmentation scale on vegetation classification. (a) shows segments with an
Normalized Difference Vegetation Index (NDVI) of greater than zero obtained from fine
NosremgmaleinzetadtiDonif faenrden (bce) fVreogme tcaotaiorsneI snedgemxe(nNtaDtiVoIn), oafngdr (eca)t iemrathgaen bezfeorroe othbeta siengemdefrnotamtiofinn eprsoecgemsse. ntation
and(b)fromcoarsesegmentation,and(c)imagebeforethesegmentationprocess.
For the remaining background classes (i.e., road, railroad, river), we classify the segments using
vecFtoorr tdhaetar.e Fmoar itnhiinsg pbuarcpkogsero, uwned alcsloa sismespl(ei.me.e,nroteadd ,ar faiinlero saedg,mrievnetar)t,iowne foclra tshseifsye tchlaessseegs.m Aefntetrs wusei ng
veccltaosrsidfiaetda .alFl obracthkgisropuunrdp oclsaes,swese, tahles oreimmapinleinmge cnlatessd (ai.efi.,n uenscelagsmsifeinedta)t hioans af ocrertthaeins epcrolabsasbeisli.tyA tfote brew e
clacslsaisfisiefdiedal lasb aac sklugmro.u Hnedrec,l awssee ism,tphleemreemnta tihnein sgeccolnadss s(tie.ep.., unclassified)hasacertainprobabilitytobe
In this second step, we re-segment the unclassified class, aiming at coarser segments. The ESP
classifiedasaslum. Here,weimplementthesecondstep.
can produce three levels of segmentation, which can be associated with three levels of slums objects
Inthissecondstep,were-segmenttheunclassifiedclass,aimingatcoarsersegments. TheESPcan
as mentioned in [5]. Since slum buildings are characterised by their small size (Table 2), it is difficult
producethreelevelsofsegmentation,whichcanbeassociatedwiththreelevelsofslumsobjectsas
to extract every single building as an object. Therefore, we use the second level of SP obtained from
mentionedin[5]. Sinceslumbuildingsarecharacterisedbytheirsmallsize(Table2),itisdifficultto
ESP, which is 95.
extracteverysinglebuildingasanobject. Therefore,weusethesecondlevelofSPobtainedfromESP,
After conducting the segmentation process, we implement our concept of slums to develop the
whichis95.
ruleset for classifying each test area. The threshold values were obtained through a trial and error
Afterconductingthesegmentationprocess,weimplementourconceptofslumstodevelopthe
process, and we assigned these values into the class description in E-cognition software (version,
rulesetforclassifyingeachtestarea. Thethresholdvalueswereobtainedthroughatrialanderror
Manufacturer, City, US State abbrev., Country) (Table 3).
process,andweassignedthesevaluesintotheclassdescriptionineCognitionsoftware(Developer
version9(TrimbleGermanyGmbTHa,bMle u3.n Tihcrhe,sGhoelrdm vaalnuye )f)or( Teaabchle ru3l)e..
Rule Threshold Value
Table3.Thresholdvalueforeachrule.
1. Border to river > 0 pixels
Association: Distance to River/Railroad
Rule 2. Border tToh rraeislhrooalddV 0a >lu peixels
1. Compactness ≤ 5
1.Bordertoriver>0pixels
Association:DistancetoRiver/Railroad
Shape: compactness 2.Bordertorail2ro. aGdr0ey>-Lpiexveelsl Co-occurrence Matrix
1.Compactness(G≤L5CM) Dissimilarity ≥ 0.0005
Shape:compactness
Tone: tile—corrugated iron, asbestos 2.Grey-LevelCMo-oecacnu rrreedn/cgerMeeantr i1x ≤(G toLnCeM ≤) 1D.i0s7si5m ilarity≥0.0005
Tone:tile—corArungcaitleldariyro dn,aatasb: eLsatonsd Use Plan (seconMd esacnenreadri/og)reenM1e≤ant oLnaey≤er1 T.0e7n5ure > 0.25
Ancillarydata:LandUsePlan(secondscenario) MeanLayerTenure>0.25
For the first rule, we use the border to the river and railroad and assign each object that has
more than zero pixels touching the border of river/railroad as a slum. Regarding shape, we
Forthefirstrule,weusethebordertotheriverandrailroadandassigneachobjectthathasmore
implement two rules, compactness and grey level co-occurrence matrix (GLCM) dissimilarity.
thanzeropixelstouchingtheborderofriver/railroadasaslum. Regardingshape, weimplement
Compactness indicates the variations among pixels under one object. The lower the compactness,
two rules, compactness and grey level co-occurrence matrix (GLCM) dissimilarity. Compactness
the higher the variation of pixel values. Regarding GLCM dissimilarity, the higher the value, the less
indicatesthevariationsamongpixelsunderoneobject. Thelowerthecompactness,thehigherthe
that the pixel values show similarity within one segment [48]. For the tone, since the roof materials of
variationofpixelvalues. RegardingGLCMdissimilarity,thehigherthevalue,thelessthatthepixel
slum houses in our study area are predominated by tiles or corrugated iron, we find that the average
valuesshowsimilaritywithinonesegment[48]. Forthetone,sincetheroofmaterialsofslumhouses
of red/green shows a linear relationship with the roof colour. Here, we use the band arithmetic
inourstudyareaarepredominatedbytilesorcorrugatediron,wefindthattheaverageofred/green
approach in E-cognition by calculating the proportion of red and green band in each segment. The
shloawsts rualel iins eoanrlyr ealpaptiloicnasbhleip tow thiteh sethcoenrdo socfencoalroiou. rT.oH deevree,lowp ethuiss erutlhe,e wbea fnirdsta croitnhvmeretetidc tahpe pzroonainchg in
eCmoganpi toifo nthbe ystcuadlcyu alarteian gfrothme pveroctpoor rttoio rnasotferr.e dThaennd, wgree ernecblaasnsidfiiend etahceh vsaelgume oefn te.aTchh elalnadst urusele cilsasosn ly
apipnltioc atbwloe ltaobtehles, sie.ec.o, hnadvsec teennaurrioe .aTnod dneov teelnoupreth. iNserxutl,e t,hwise bfiinrastryc oimnvaegret eisd stehgemzeonntiendg umsianpg oMfRthSe (tshteu dy
arezaonfrinogm mvaepc tworast onorat sutseerd. Twhitehni,nw theer seecglamsesnifitaetdiotnh aesv naolut aelol sfceeancahriloasn wdeures euscilnagss thinet zootnwinogl mabaepls a,si .e.,
ha vetenureandnotenure. Next,thisbinaryimageissegmentedusingMRS(thezoningmapwas
RemoteSens.2017,9,1164 10of17
notusedwithinthesegmentationasnotallscenarioswereusingthezoningmapasancillarydata).
Remote Sens. 2017, 9, 1164 10 of 17
Wecalculatethe‘tenurevalue’ofeachsegmentandidentifythethresholdforslums. Themorethe
segmentedimageoverlappedwiththe‘tenuresegment’,thehigherthechancethatthesegmentis
ancillary data). We calculate the ‘tenure value’ of each segment and identify the threshold for slums.
aslum.
The more the segmented image overlapped with the ‘tenure segment’, the higher the chance that the
Wsegemuesnet “isO aR s”lufmu.n ctionforassociationinourruleset,whichmeansthataslummaybelocated
neartherWivee ru,soer “nOeRar” tfhuencrtaioilnro faodr ,aossroicniatthioenp inro oxuimr riutylesoeft,b wothhicohf mtheeamns. tMhaeta an swluhmil em,faoyr bteh elorceastetdo fthe
indicanteoarrs t,hwe reivuesre, othr ene“aAr tNheD r”aifluronacdti, oonr ,inw thhiec phrmoxeimanitsy tohfa btotthhe oof bthjeecmt.m Muesatnmwheeilte,a flolrt hthree srhesotl odf vthaelu eto
beclainssdiificaetdorass, wsleu umsse. the “AND” function, which means that the object must meet all threshold value to
be classified as slums.
4.3. RulesetImplementation
4.3. Ruleset Implementation
Weimplementourrulesetinthefirsttestarea(clearboundariesbetweenslumandnon-slum),and
We implement our ruleset in the first test area (clear boundaries between slum and non-slum),
thesecondarea(unclearboundaries). Inaddition,weimplementourrulesetfortwoscenarios,first
and the second area (unclear boundaries). In addition, we implement our ruleset for two scenarios,
withusingtenurestatusasanadditionalproxy,andsecondwithouttenurestatus.Hence,thefourpairs
first with using tenure status as an additional proxy, and second without tenure status. Hence, the
ofresultsareshowninFigure7.
four pairs of results are shown in Figure 7.
(a) (b)
(c) (d)
Figure 7. Mapped slums in the first test area (a,b). (a) indicates slums without the tenure indicator
Figure7. Mappedslumsinthefirsttestarea(a,b). (a)indicatesslumswithoutthetenureindicator
(only consider explicit indicators); (b) indicates slum employing the tenure indicator. Meanwhile,
(onlyconsiderexplicitindicators); (b)indicatesslumemployingthetenureindicator. Meanwhile,
(c,d) indicates slums in the second area, by including and excluding the tenure indicator,
(c,d)indicatesslumsinthesecondarea,byincludingandexcludingthetenureindicator,respectively.
respectively.