Automated Damage Indication for Rapid Geospatial Reporting

PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING Sep t embe r 2011 1

Dirk Tiede, Stefan Lang, Petra Füreder, Daniel Hölbling, andPeter Zeil are with the Centre for Geoinformatics, Universityof Salzburg, Schillerstr. 30, 5020 Salzburg, Austria([email protected]).

Christian Hoffmann is with the GeoSpatial Division, TrimbleGermany GmbH, Trappentreustr. 1, 80339 Munich, Germany.

Photogrammetric Engineering & Remote Sensing Vol. 77. No 9, September 2011, pp. 000–000.

0099-1112/11/7709–0000/$3.00/0© 2011 American Society for Photogrammetry

and Remote Sensing

AbstractA methodology for automated extraction of damage indica-tion from very high spatial resolution satellite imagery ispresented for the Haitian towns of Carrefour and Léogânefollowing the January 2010 earthquake. Damaged buildingsare identified by changes to their shadows between pre- andpost-event data. The approach makes use of object-basedimage analysis concepts to extract relevant information ondamage distribution. The methodology selected requires pre-and post-disaster images with similar sun angles and willonly be suitable for the detection of collapsed (or partlycollapsed) structures. The new methodology proved to beeffective for the Carrefour area despite using satellite imagesof different qualities with inadequate image (co-)registration,producing positively validated results within an acceptableprocessing time, and was, to the best of our knowledge, theonly automated damage assessment method to have deliv-ered appropriate results to requesting relief organizationswithin a few days of the Haiti earthquake.

IntroductionGeospatial information products provided in the context ofdisasters need to meet certain criteria if they are to beappropriate and useful. These criteria include the need to be(a) effective and reliable, and (b) able to meet the user’srequirements (Lang et al., 2009). The need for effectiveness asa function of thematic and geo-positional accuracy, andtimeliness, has resulted in operators trying to partiallyautomate the processing steps that lead to the requiredproducts. Recent achievements have increased the operationallevel of such crisis mapping products, at least at the experi-mental stage. However, both users and service providers areconcerned about the trustworthiness of automated products ingeneral, and about aspects of their reliability, timeliness, andeffectiveness in particular. While there is increasing demandfor supporting time-consuming and work-intensive visualinterpretation, the practical and operational use of automatedtechniques is limited (Lang et al., 2010b).

For particular target object domains such as in facerecognition, material surface scanning, or ship detection, thesoftware algorithms are tuned to detect specific objects, buteven then automated techniques need to be optimized. Incomplex situations such as exist after an earthquake, withmany kinds of man-made and natural features in differentstages of destruction, the number of potential features to bedetected is far greater, and they are therefore far more

Automated Damage Indication forRapid Geospatial Reporting

Dirk Tiede, Stefan Lang, Petra Füreder, Daniel Hölbling, Christian Hoffmann, and Peter Zeil

difficult to capture by fully automated techniques. If thespectral characteristics of target features are sufficientlydistinct, pixel-based signal processing may be able totransform those features into the required informationclasses (Lang et al., 2010b).Within certain signal-relatedlimits, the most straightforward case is a binary distinctionbetween class and non-class. Binary masking, such as theinundated area after a flood event derived from syntheticaperture radar (SAR) imagery, ranks among the most highlyreliable products in rapid geospatial reporting. However, thegreater the complexity of a scenario, the greater the apparentsuperiority of human visual perception over a softwarealgorithm, especially when time is a critical factor.

Within object-based image analysis (OBIA), the assessmentof the product goes beyond positional and thematic accuracy.The term object validity (Lang et al., 2010a) refers to a higherconceptual level of matching that goes beyond a dichotomousright or wrong and evaluates whether or not object provisionis appropriate. There are two aspects to object validity: firstly,in the disaster mapping context we encounter an entire rangefrom bona fide to composite fiat objects, the latter having ahigh degree of freedom in object composition and delineation,and secondly, in addition to positional accuracy (which ismainly influenced by image registration and spatial referenc-ing) the spatial appropriateness of objects is a matter ofgeneralisation and scale-specific delineation.

In this paper we focus on an automated approach usedfor the rapid production of damage indication maps follow-ing the Haiti earthquake on 12 January 2010. The termdamage indication implies that the products deliver criticalinformation on an aggregated scale, beyond individualdestroyed structures. Since it is based on a single indicator(in this case shadow, as described below), the product doesnot aim to provide local damage maps but rather aims toprovide a rapid indication of where higher or lower damagedensities can be expected, which needs to be borne in mindwhen validating such products.

The damage indication maps for the Haitian towns ofCarrefour and Léogâne were derived from very high spatial res-olution (VHSR) satellite imagery (GeoEye-1 and WorldView-2)and delivered within the Rapid Geospatial Reporting Service ofthe EU’s G-MOSAIC (GMES services for Management of Opera-tions, Situation Awareness and Intelligence for Regional Crises;http://www.gmes-gmosaic.eu/) FP7 Research Project. Theservice was initially requested by the Cartographic Section ofthe United Nations Department of Field Support (UN-DFS) andthe Spanish Red Cross, in order to urgently receive geo-spatialproducts that would assist the relief efforts in Haiti.

Study Area and DataThe two coastal communities of Carrefour and Léogâne arepart of the Ouest Department of Haiti. Carrefour is locatedabout 6 km west of the capital Port-au-Prince and forms partof the Port-au-Prince metropolitan area. As the secondlargest city in Haiti, it had a calculated population estimateof around 430,000 in 2009 (IHSI, 2009). The denselypopulated city extends from the coastal lowlands to thehillsides flanking the mountains to the south. According toUN-HABITAT (n.d.), Carrefour can be described as a middleclass suburb of Port-au-Prince with rapidly expanding areasof informal settlement. Léogâne lies about 30 km west ofPort-au-Prince and was only 12 km north-west of theearthquake epicenter (USGS, 2010); see Figure 1. Thecalculated population estimate for Léogâne in 2009 wasaround 78,000 (IHSI, 2009).

The damage assessment made use of optical VHSR imageryfrom before and after the event made available within the G-MOSAIC Rapid Geospatial Reporting Service shortly after theearthquake. The damage analysis for Carrefour was based onGeoEye-1 images acquired on 27 July 2009 (pre-disaster image)and 13 January 2010 (post-disaster image). For both the pre-and post-disaster data, a multispectral image with three opticalbands and a near infrared (NIR) band, and a panchromaticimage, were available for the analysis. The spatial resolutionof the panchromatic image, which is normally 0.41 m, was re-sampled to 0.5 m due to US Government restrictions oncivilian imaging. The multispectral image was also down-sampled from 1.65 m to 2 m. For Léogâne a pre-event GeoEye-1 image from 01 October 2009 and a post-event World-View-2image from 15 January 2010 were used for the analysis. Bothof these images for Léogâne were delivered in pan-sharpenedformat with a spatial resolution of 0.5 m (the 0.46 m spatialresolution of the WorldView-2 panchromatic image was alsore-sampled to the 0.5 m limit for non US Government cus-tomers) but only RGB bands were made available (see Table 1).Furthermore, the radiometric resolution, which was originally

11-bit, was reduced to 8-bit for the Léogâne area. All imageswere devoid of additional metadata or any documentation onthe pre-processing steps.

Problem Definition and State of the ArtSpecific automated image analysis techniques based on VHSRimages have reached a state of maturity that allows them tobe used for applications in which reliability and timelinessare crucial (Lang et al., 2010b). Several studies have demon-strated different approaches that can lead to successfulapplications in the field of damage assessment using changedetection methodologies such as the comparison of pre- andpost-event images. Gusella et al. (2005) demonstrated the useof QuickBird data for quantifying the number of buildingsthat had collapsed as a result of the 2003 Bam earthquake inIran. Also using QuickBird data, Pesaresi et al. (2007)showed that rapid damage assessment of built-up structuresin tsunami-affected areas is possible with very high accura-cies based on a multi-criteria recognition system with arestricted set of general assumptions (e.g., standing structurescast a shadow while collapsed structures no longer cast ashadow and leave debris on the ground). Vu and Ban (2010)developed a context-based automated approach for earth-quake damage mapping that relied on the identification ofdebris areas after the 2008 Sichuan earthquake in China withthe calculation being speed optimized by parallel processingimplementation, which produced good results for the testarea. Ehrlich et al. (2009) and Brunner et al. (2010) demon-strated the usefulness of applications that made additionaluse of synthetic aperture radar (SAR) data for damagedbuilding assessment, also in the Sichuan earthquake area.Three dimensional information has also been taken intoaccount to detect damaged built-up structures followingearthquakes by Turker and Cetinkaya (2005), with the helpof stereo-images. However, for rapid damage assessment soonafter an earthquake the requirement for stereo-images can bean additional obstacle: simply obtaining of a pair of matching

2 Sep t embe r 2011 PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING

Figure 1. Overview of the areas under investigation to the west of Port-au-Prince, Haiti.

pre- and post-disaster images is usually difficult enough onits own. Broad overviews of automated techniques forearthquake damage detection have been provided by Chini(2009) and by Rathje and Adams (2008).

None of the above approaches have, however, yet beenapplied at an operational level in the relief phase shortlyafter a disaster. One reason may be the critical requirementfor good quality pre-processed data, i.e., mainly for highlyreliable image (co-)registration. For most of the automatedchange detection algorithms this is a crucial point (Lu et al.,2004; Sundaresan et al., 2007). Immediately following adisaster there is often a lack of high quality pre- and post-disaster VHSR imagery available (refer to Rathje and Adams,2008). The availability of VHSR satellite data after the Haitiearthquake in January 2010 was, however, outstandingcompared to previous disasters. Data have even beenprovided free of charge to the general public (also outsidethe International Charter on Space and Major Disasters) bycompanies such as Google® or DigitalGlobe®. One of theconsequences was that a large quantity of ’crowd-sourced’information was available in the aftermath of the earthquake.Nevertheless, such datasets are often not well suited forautomated analysis methods because of, for example, thelack of metadata, un-documented pre-processing, insufficientpre-processing, or as was the case for Léogâne, a missing NIRband.

Al-Khudhairy et al. (2005) described automated assess-ments of structural damage caused by war-like conflictsusing Ikonos data, concluding that visual comparison of veryhigh spatial resolution pre- and post-crisis images remainedthe fastest method for rapid damage assessment withinhours or days of humanitarian crises. The reason for thisconclusion was that use of the tested algorithms required agreat deal of experience and skill in image analysis as wellas considerable expenditure of human labor and time todetermine the appropriate settings and approaches.

Despite all of the aforementioned limitations, we believethat automated algorithms can be successfully applied torender urgent estimations of damage intensities (even usingthe earliest available VHSR satellite images) to provideeffective assistance to relief efforts. The process describedbelow presents an approach which (a) is operational evenwith VHSR images of different qualities, i.e., especially withregard to image (co-)registration problems, (b) is built onbasic rulesets that are easily adapted to the specific condi-tions in hand, and (c) produces results within an acceptableprocessing time.

MethodsRule-based Classification and Class ModelingThe automated approach relies on object-based imageanalysis (OBIA) to extract relevant information as an indica-tion of damaged buildings. OBIA provides a methodological

framework for machine-based interpretation of complexclasses defined by spectral, spatial, structural, and hierarchi-cal properties (Lang et al., 2010a). Rulesets for automateddamage indication and change detection are coded in CNL(Cognition Network Language) using eCognition® 8 software,which offers a modular programming environment for(image-) object handling within a vertical and horizontalhierarchy. Rule-based classifiers are used for knowledgerepresentation, making explicit the required spectral andgeometrical properties as well as spatial relationships foradvanced class modeling (Tiede et al., 2010). An overviewof the workflow is shown in Figure 2, and the individualsteps are described below.

Automated Damage IndicationDamage assessment from remote sensing data is alwayshighly dependent on the quality of the available imagery.This is particularly true for automated approaches, sincethe focus lies on very distinct and detectable changes.Furthermore, the intrinsic and intuitive knowledge of askilled interpreter is difficult to translate into a system ofrules. The effective transfer of experience into proceduraland structural information is one of the major challengesfaced (Lang, 2008).


TABLE 1. AVAILABLE PRE- AND POST-EVENT SATELLITE IMAGES FOR CARREFOUR AND LÉOGÂNE

Sensor Acquisition date Spatial resolution Spectral resolution

Carrefour pre-event image GeoEye-1 27 July 2009 2 m multispectral, 4 multispectral bands � 10.5 m panchromatic panchromatic band

Carrefour post-event image GeoEye-1 13 January 2010 2 m multispectral, 4 multispectral bands � 10.5 m panchromatic panchromatic band

Léogâne pre-event image GeoEye-1 01 October 2009 0.5 m pansharpened 3 multispectral bands (NIR missing)

Léogâne post-event image WorldView-2 15 January 2010 0.5 m pansharpened 3 multispectral bands (NIR missing)

Figure 2. Workflow for automated damage indication

Because of the tight time frame in emergency situationsadditional challenges, in particular those affecting thequality of the imagery, have to be tackled. In this particularcase these challenges included:

• Different recording conditions for appropriately timedavailable images, e.g., different viewing angles, or differentsun azimuths and elevations due to seasonal variations.

• Time constraints in preprocessing of the imagery, resultingin limited geometric accuracy, a mismatch between pre-and post-event images (geometric shift), and radiometricvariations.

• Some of the available imagery lacked the fourth (NIR)spectral band.

• GeoEye-1’s 0.41 m and WorldView-2’s 0.46 m spatialresolution in the panchromatic band was down-sampled bythe provider to 0.5 m due to US Government restrictions oncivilian imaging, which even applied to this humanitarianrelief effort.

Using Building Shadows as Damage IndicatorsRulesets for feature extraction and change detectionhave been developed in the context of previous researchprojects (Tiede and Lang, 2008a; Lang et al., 2010). Thetransferability of such knowledge-based rulesets is limiteddue to the complexity of the extractable features andvariations in the available imagery (spectral and spatialresolution, preprocessing, etc.). An adaptation is therefore

required, providing the opportunity to react to specificconstraints. In the case of the Haiti earthquake, visualinspection of the available imagery only few days after theearthquake (following the request by the users to the G-MOSAIC Project) revealed that an automated approachwould not be feasible for individual dwelling detection andcategorization within an appropriate time frame. This wasnot only due to the quality of the imagery (see list above)but also to the very dense arrangement of small houses,especially in the Carrefour area.

A more promising possibility was the use of damageindicators, which in this case were the changes in theshadows cast by buildings before and after the earthquake(see Bitelli et al., 2004; Turker and Sumer, 2008). Theselected methodology has a prerequisite of pre- and post-disaster images with similar recording angles and is onlysuitable for the detection of collapsed (or partly collapsed)structures (see Figure 3).

Masking VegetationIn order to avoid false positives from vegetation shadows avegetation mask was created for both images (see Vu et al.,2004). For the Carrefour area the vegetation mask was basedon the Normalized Differenced Vegetation Index (NDVI).Missing NIR information in the available satellite images forLéogâne could be partly compensated by using visiblegreenness instead of the NDVI.


Figure 3. (a) Shadows cast by buildings and walls before the earthquake, and (b) theabsence of these shadows after the earthquake due to destruction of the structures,in the Carrefour area (top: GeoEye-1 images) and the Léogâne area, (bottom: GeoEye-1pre-image and WorldView-2 post-image)

Overcoming Shifts between Pre- and Post-event Images: Object-linkingPrecise geometric registration between multi-temporalimages is reported to be crucial to automated changedetection (Lu et al., 2004). Sundaresan et al (2007) examinedthe robustness of two change detection algorithms (imagedifferencing and MRF-based change detection) in the pres-ence of registration errors in remote sensing images andconcluded that, for satisfactory performance of imagedifferencing, the registration error should not exceed 0.2pixels (RMS). In the present case the available VHSR datawere provided in a preprocessed mode without metadata onprocessing levels, hampering a rapid image-to-image orimage-to-map registration, especially when no accuratereference data sets were available (see also Bitelli et al.,2004; Rathje and Adams, 2008). The shift between the multi-temporal images was up to 5 meters and varied throughoutthe images. Human observers conducting rapid visualdamage assessment can generally resolve such geometricerrors in image registration by recognizing similaritiesbetween identified objects, logical coherence, and patterns.In our approach we decided to avoid time-consumingadditional (co-)registration of the images by introducing anobject-linking approach. After the damage indications hadbeen extracted during the change analysis we applied anobject-by-object comparison, which allows the linking ofsimilarly shaped objects found in the pre- and post-eventimages using spatial queries including buffers in x or ydirections. For example, if a shadow object of a certain size

and shape identified in the pre-event image is partly(geospatially) overlapping a shadow object identified in thepost-event image, the size and shape of the two objects iscompared and, if sufficiently similar, the object is consid-ered to be still in existence. In this case, the shape indexwas used as a form descriptor independent of the actualobject size. The shape index (SI) is commonly defined as thedeviation of an object’s shape from a circle (or square) of thesame size. Shape index values range from 1 (for a perfectcircle) to higher values (for an irregular shape). Overlappingof objects can be considered as a direct overlap or anoverlap including a virtual buffering in a certain direction(in this case up to 10 pixels in westerly and northerlydirections, which corresponds to a relative shift of approxi-mately 5 meters in a north-westerly direction). Figure 4shows the shift between the pre-and post-event images andthe linking of objects due to their similarity.

Unambiguous vegetation shadows were rejected usingthe spatial relationships between shadow objects andneighboring objects. If a shadow object had only adjacentobjects that had been classified as vegetation (in the sundirection) the change detection analysis for these shadowobjects was ignored.

Distributed ComputingDistributed computing enabled rapid processing of the datasets. A multi-core blade server was available for the analy-sis, which reduced the processing time. For distributed


Figure 4. Conceptual illustration of the object-linking approach, comparing extracted shadowobjects from the pre- and post-disaster images despite a geometrical shift between theimages. In this case the shadow is considered to be still in existence (no indication ofdestruction of the building) since the area and form descriptors are quite similar.

processing we used the eCognition® server software. Thisallows the tiling of images, the distribution of image tiles todifferent computer nodes, the processing of image tiles usingthe developed ruleset, and a merging and post-processing ofresults on the host computer (see Tiede and Lang, 2008b).

Damage Indication MapsThe extracted damage indicators for the two areas wereanalyzed and conditioned by applying kernel densitymethods (Silverman, 1986), since the damage indicatorshave only a limited significance for absolute damage figures.The kernel density was calculated using point features (inthis case the centroids of the extracted damage indicatorobjects) on an output raster cell size of 20 m � 20 m. Theresulting maps (damage indicator maps) are intended toprovide a rapid and easy-to-grasp overview of the spatialdistribution and intensity of damage within the studiedareas.

ValidationValidation includes more than just the verification or qualitycontrol of the product. The verification, usually based on anassessment of the product’s accuracy, will assure that theproduction process is consistent with pre-defined qualityrequirements and provide evidence to confirm that theproducts are delivering the correct information. The applica-tion of standard accuracy assessment routines for thesekinds of maps is, however, limited (Kerle, 2010). Thevalidation process applied in this study comprised threesteps prior to the products undergoing user validation in aworkshop. The initial step involved an internal projectquality check by the European Union Satellite Centre (EUSC)service chain leader before delivery to users, comprisingplausibility tests and spot checks. For the second step, anaccuracy assessment was conducted for 100 randomlyselected damage indicators from each location, and visuallycross-checked on the same data sets. Due to the short time-frame these tests were only able to provide a rough valida-tion of the user’s accuracy. The third validation step was acomparison with the Haiti Earthquake 2010 “RemoteSensing Damage Assessment: UNITAR/UNOSAT, EC, JRC, andWorld Bank” several weeks after the event.

This reference dataset has been produced in support ofthe Post Disaster Needs Assessment and Recovery Frame-work (PDNA) and is a joint collaboration between the UnitedNations Institute for Training and Research (UNITAR), theOperational Satellite Applications Programme (UNOSAT), theEuropean Commission Joint Research Centre (EC JRC), and theWorld Bank. This manual damage assessment was based onsatellite imagery and aerial photos; the first results forCarrefour were available at the end of February 2010(UNITAR/UNOSAT, 2010). Damage was visually classifiedinto 5 grades from 1 (negligible to slight damage) to 5(destruction), based on the European Macroseismic Scale(EMS-98) five-level grading system, but had not yet beenvalidated on the ground (in the publically available Version 3).Nevertheless, this damage atlas provides a much morecomprehensive overview of the damage than other referencedata sets for this area. The comparison of the results fromthis data set with the automated results focused on thecorrect identification of damage intensity and damagedistributions. This was necessary because, firstly, theproducts had different original purposes (rapid geospatialreporting to assist relief efforts, as opposed to support of apost-disaster needs assessment) and therefore used differentdata and were produced under different time constraints,and secondly, the automated approach was restricted to thedetection of shadows and changes in shadows, and did notconsider individual buildings. In order to compare the

damage intensities a kernel density map was derived fromthe reference data set, taking into account the damageclassified as Grade 4 (very heavy damage) and Grade 5(destruction), which is likely to have affected the shadowscast by buildings. The kernel densities in the two mapswhere categorized into four different classes based on the25 percent quartiles, plus a fifth class for those areas withno damage. The comparison between the two maps wasevaluated using a rank difference calculation, which calcu-lates the difference in rank between the classes for eachpixel, ascribing values ranging from 0 (same class in thereference map as in the automated calculated map) to �4 or24 (widely diverging classifications). The evaluation wascarried out using a fuzzy approach, accepting neighboringclasses (rank difference between -1 and �1) as still valid.Congalton and Green (2009) stated that such an approach isonly acceptable if the classes are very similar or evencontinuous (as are the damage density and damage indica-tion classes in this case) and not discrete. Since only thedamage distributions and damage densities need to bevalidated and not the two damage assessments (which eachhave a completely different focus), we argue that thisvalidation approach can provide evidence for the relevanceand usefulness of the rapidly produced automated damageindication map in an operational context, bearing in mindthe inherent limitations.

Results and DiscussionPre-checked Published ResultsThe resulting maps for the Carrefour and Léogâne areas weresubjected to the internal project quality check by the servicechain leader. The Carrefour map passed the quality checkand was published and delivered to the users on 19 January2010. Figure 5 shows the results categorized into the discreteclasses used in the validation process. The published mapproduct is available at ReliefWeb (www.reliefweb.int) or theG-MOSAIC project website (http://www.gmes-gmosaic.eu/haiti.html).

The timeframe for the production of the Carrefourdamage indication map was around 12 hours (with twopeople involved) from the delivery of the preprocessedimagery. Most of the time was required for the initialdevelopment and adjustment of the information extractionruleset. Distributed computing enabled rapid processing ofthe data sets. The multi-core blade server used for theanalysis reduced the processing time for the Carrefour area(17 km2 and 0.5 m ground sample distance) to approxi-mately four minutes. If the calculation is extended to largerareas, the production time does not increase in a linearmanner since the most time consuming part, which is theruleset development, has only to be taken into account once(for the initial development) as, for example, was shown tobe the case in the production of the unpublished Léogânemap (see the next subsection). If the image conditions arethe same for other areas, including larger areas, the timerequired for adaptation of the ruleset will be even furtherreduced.

Verification-based on Visual InspectionThe thematic accuracy assessment for 100 randomly selecteddamage indicators from each location revealed a user’saccuracy of more than 72 percent for the Carrefour area andmore than 80 percent for the Léogâne area. Erroneousdamage detection was found to have occurred in the centralarea of Carrefour, which resulted from the effects of slightlydifferent viewing angles on the shadows cast by the tallerbuildings in this area, producing differences between the


pre- and post-event imagery. This could be avoided in futurethrough threshold modifications in the ruleset.

As well as the reasonably high user accuracy, theinternal project quality check for the Léogâne damageindication map gave good results in the outskirts of the city,but noticeably underestimated the damage in the city center.Two main reasons were identified for this underestimation:(a) the WorldView-2 images for Léogâne were only providedwith three spectral bands (RGB) and down-sampled to 8-bitradiometric resolution: the absence of the NIR band ham-pered the differentiation between vegetation or vegetationshadow and some of the buildings, and (b) the large amountof pre-event construction work in the area led to undesiredresults (as was also the case with visual interpretation ofmono-temporal post-event images (see Figure 6). This mapwas therefore not delivered to the user, but it was usedinternally to support the manual assessment in this area forcross-checking purposes.

Validation Using a Reference Data SetFigure 7 shows the rank difference map for the Carrefourarea, produced from the comparison with the Haiti Earth-quake 2010 “Remote Sensing Damage Assessment:UNITAR/UNOSAT, EC JRC, and World Bank” reference data set.Negative values indicate errors of commission: for example,if the automated approach shows the highest damage

intensity class, and the reference data set shows no damagethe rank difference will be “-4”. Positive values, on the otherhand, indicate errors of omission.

The results for each rank class are shown in Figure 8.A one-to-one class matching occurs over almost 44 percentof the area (�20,000 raster cells). Since we want to compareonly similarity of the damage distribution and damagedensity of the two differently focused map products, using afuzzy approach in which neighboring classes (rank differ-ence between -1 and �1) are still counted as valid increasesthe agreement rate to 78.4 percent. Complete disagreements(highest damage density versus no damage) between the twoproducts are rare, with the combined -4 and �4 rankdifference classes amounting to only around 0.06 percent ofthe raster density cells compared. Rank difference classes of�/-3 and �/-4 together add up to a total of 5.37 percent.These differences show an interesting distribution (seeFigure 7): while errors of commission in the automatedapproach are focused on a central area running from west toeast, the highest errors of omission occur in the hilly areasin the south-western part of Carrefour and in the north-western part of the city.

The high error of commission in the central area can beat least partly explained by the shadow problems caused bytall buildings, where the ruleset failed to take into accountthe shadow differences in tall buildings due to the slight


Figure 5. Automated damage intensity map of Carrefour underlain by the GeoEye-1 post-disaster image


Figure 7. Rank differences between the two damage density maps for theCarrefour area. Rank differences between �1 and �1 are considered by thefuzzy approach to be still valid and are thus all displayed in white. Negativevalues indicate errors of commission, i.e., higher damage densities in theautomated approach than in the reference data set; positive values indicateerrors of omission.

Figure 6. Construction work in the Léogâne area between the two image acquisitiondates which caused undesired change detection results in the automated approach:(a) GeoEye-1 pre-disaster image, and (b) WorldView-2 post-disaster image. Black circlesindicate new buildings.

variations in viewing angle between the pre- and post-disaster images. Errors of omission in the hilly areas arelikely to reflect a problem with the quality of the imageryorthorectification.

Further problems that were identified as affecting theresults of the automated analysis were (a) the shift betweenthe pre- and post-disaster images was not consistentthroughout the images, i.e., smaller shadow objects (abovethe size thresholds) were sometimes detected as changes dueto an increased shift within a certain part of the image, (b)in very densely built-up areas such as Carrefour, the possi-bility of identifying shadows from individual houses islimited and sometimes only shadows from large blocks canbe analyzed, which leads to an underestimation of damagewithin these areas, and (c) unlike manual interpretation, thisautomated approach is limited to shadow detection. Possibleimprovements by taking into account additional indicators(such as piles of debris) are yet to be implemented.

Despite these limitations the comparison shows a goodagreement overall for a quick estimation of damage intensitydistribution, especially considering that the focus was onproducing geospatial products as a matter of urgency, basedon the earliest available VHSR satellite images, in order toassist the relief efforts in Haiti. It also demonstrates theimportance of these products as effective accompaniments toon-going relief efforts. The automated damage indicationmap is not able to replace “in depth” damage assessments,nor was that ever the intention; its aim is rather to providepreliminary (but reliable) indications of damage distributionfor initial disaster relief operations.

User ValidationThe damage indication product and the underlying approachwere discussed in detail with the user community and inparticular with the Cartographic Section of the UnitedNations Department of Field Support (UN-DFS), who activatedthe G-MOSAIC Rapid Geospatial Reporting service. The returnon the products was very positive. In addition this userconsultation process revealed that in the early phase of adisaster any helpful information is appreciated, even if ithas a lower site-specific accuracy due to the aggregationlevel of the product. However, the reliability or assumedaccuracy range should be explicitly stated on the product.Based on this feedback, Lang et al. (2010b) proposed areliability ranking scheme for rapid geospatial reporting,based on critical self-assessment by the service provider.This is especially helpful for applications where time and

resources are limited, resulting in products whose reliabilitycannot be based on independent validation procedures.

ConclusionsThe proposed methodology was, to the best of our knowl-edge, the only automated damage assessment method thatdelivered results to requesting relief organizations within thefirst few days after the Haiti earthquake, in order to assistrescue efforts. Following acquisition of the pre- and post-disaster images, it took only 12 hours to complete theruleset development / adaptation and damage indicationcalculations for the Carrefour area.

The methodology is optimized to conditions where (a)pre-and post-disaster images have a similar viewing angleand a fourth NIR channel is available, (b) the buildings castshadows, and (c) damage indications are calculated forcollapsed buildings (casting either no shadow or a muchsmaller shadow).

This approach is able to overcome some of the problemsthat often occur in rapid damage assessment scenarios inthat (a) perfect matching of the images is not required assmall shifts can be accommodated through object linking, (b)data from different VHSR sensors can be compared, and (c)parameterization of the ruleset and the final processing canbe performed sufficiently rapidly for it to be used in anoperational context.

Finally, it should be emphasized that the automatedapproach presented herein is not designed to extractabsolute values concerning damaged buildings, nor is it ableto completely replace manual interpretation. Its strength liesin the ability for rapid information extraction (if the method-ological assumptions hold true), thereby assisting users andmanual interpreters to more quickly obtain an impression ofthe spatial distribution of damage in emergency situationsand providing a guide for further, more detailed, analyses.

AcknowledgmentsThis work has been funded by the European Commission(EC) within the FP-7 G-MOSAIC Project (GMES Services forManagement of Operations, Situation Awareness andIntelligence for Regional Crises, Contract No. 218822). Wewould like to express our thanks to those project partnersinvolved in the work package” Geo-spatial support for CrisisManagement Operations” and in particular to the projectpartners EUSC and e-Geos. We would also like to thankTrimble for providing access to their eCognition® servercluster supporting the rapid processing of the data sets.

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Documents

Automated Damage Indication for Rapid Geospatial Reporting