DETECTION AND ANALYSIS OF BUILDING DAMAGE CAUSED BY EARTHQUAKES USING LASER SCANNING DATA. Miriam Rehor 1, Hans-Peter Bähr 1

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1 International Symposium on Strong Vrancea Earthquakes and Risk Mitigation Oct. 4-6, 2007, Bucharest, Romania DETECTION AND ANALYSIS OF BUILDING DAMAGE CAUSED BY EARTHQUAKES USING LASER SCANNING DATA Miriam Rehor 1, Hans-Peter Bähr 1 ABSTRACT After earthquakes it is essential to obtain information about the damage situation of affected areas rapidly. For this purpose, airborne laser scanning is very suitable because it allows the fast acquisition of height data for large areas without the necessity of entering the affected areas. In order to employ the available search and rescue resources adequately for saving as many lives as possible, it is advantageous to know which buildings are damaged and how they are collapsed. Therefore, this paper presents an automatic building damage detection and classification method based on the comparison of pre-event building models composed of planar surfaces with planar surfaces extracted from laser scanning data acquired rapidly after the disaster. Features like volume and height reduction or the inclination change of building surfaces can be derived by superposing the planar surfaces of both states. By means of these parameters and knowledge about the characteristics of different damage types composed in a so-called damage catalogue, a fuzzy logic classification of building damage can be carried out. The results of this damage interpretation represent one main input into the Disaster Management Tool (DMT) which was developed to support decision makers as well as surveillance and intervention teams during disaster response. The results achieved by testing the developed approach on data containing buildings with real damage are presented and analysed. INTRODUCTION Strong earthquakes cause many casualties every year. Many people die because they are trapped in collapsed buildings and cannot be rescued in time. Since the number of trapped victims and the resources needed for rescue activities depend amongst others on the damage types occurring on buildings (Schweier, 2007), it can be very helpful if knowledge about the damage situation is available rapidly after the disaster. Therefore, one project of the Collaborative Research Centre (CRC) 461 Strong Earthquakes: A Challenge for Geosciences and Civil Engineering deals with the automatic detection and classification of building damage after strong earthquakes (Rehor, 2007). This damage analysis is presented in this paper. It is based on the comparison of pre-event building models composed of planar surfaces with planar surfaces derived from post-event airborne laser scanning data. Airborne LIDAR data are used because the technique of laser scanning allows a rapid and extensive acquisition of height data without the necessity of entering affected areas. For more details about the technique of airborne laser scanning see for example Wehr and Lohr (1999). The use of LIDAR data for the detection of collapsed buildings after disasters has already been proposed in several publications, e.g. by Murakami et al. (1998), Vögtle and Steinle (2004), Vu et al. (2004). In contrary to other approaches dealing with damage assessment based on high resolution satellite (e.g. Gusella et al., 2005; Chesnel et al., 2007) or aerial images (e.g. Sumer and Turker, 2006), the height component can also be included in the analysis if LIDAR data are used. As a consequence, damage types that cannot be detected in 2D data can then be identified, e.g. pancake collapses. 1 Institute of Photogrammetry and Remote Sensing, Universität Karlsruhe (TH), Englerstr. 7, Karlsruhe, Germany (miriam.rehor, hans-peter.baehr)@ipf.uni-karlsruhe.de

2 458 M. Rehor & H.-P. Bähr The problem of damage detection methods based on LIDAR data is the difficulty to acquire multitemporal data sets of areas affected by disasters like earthquakes since their occurence is not yet predictable. Due to this, the approaches mentioned above have originally been developed for the detection of changes in urban areas and have never been tested on data containing real building damage until now. As aerial stereo images of areas containing damaged buildings are available, Turker and Cetinkaya (2005) as well as Rezaeian and Grün (2007) use digital surface models (DSM) generated from such stereo images for their damage detection methods. The disadvantage of these DSMs is that their height component is approximately ten times worse than the height component of DSMs acquired by laser scanning. The results of the damage interpretation method described in this paper represent one main input into the Disaster Management Tool (DMT). The DMT has also been developed within the CRC 461 in order to support decision makers as well as surveillance and intervention teams during disaster response (Gehbauer et al., 2007). The casualty estimation method for example is based on the results of the damage analysis (Schweier, 2007). This paper starts with a description of the damage types which are distinguished in the classification. Subsequently, the used data containing buildings with different damage types are presented. Hereafter, the whole workflow of the segment based fuzzy logic classification process is explained. Finally, the results achieved by applying the method on the test data are presented and analysed. DAMAGE TYPES At the beginning of every classification process, classes have to be defined that shall be discriminated. Therefore, a so-called damage catalogue was developed in cooperation with another project of the CRC 461 (cf. Schweier and Markus, 2004, 2006). It contains the different damage types appearing on buildings after earthquakes. Fig. 1 shows a composition of these damage types. For every damage type qualitative and quantitative values for geometrical characteristics (e.g. volume reduction, height reduction, change of the inclination of building surfaces, surface structure) were determined by analysing pictures of damaged buildings. An example for qualitative information is the small volume reduction of a multi layer collapse or the irregular surface structure of a heap of debris. In these cases the qualitative specifications small and irregular are called linguistic terms of the linguistic variables volume reduction and surface structure. In contrary, quantitative information is described by intervals defined by numeric values. A heap of debris with planes has for example a volume reduction of 45% - 80%. For the definition of the damage types, it was taken into account that the geometrical features can be extracted from airborne data (e.g. laser scanning) and that rescue activities can be supported by their knowledge. At the current state only changes within the outlines of the pre-event buildings are analysed by the interpretation method. Therefore, features like the debris structure outside the footprint cannot be used for the discrimination of the different damage types. Due to this, damage types like outspread multi layer collapses or overturn collapses that are characterised especially by these features cannot be identified very well. In further research the situation outside the area of the reference building should also be analysed, e.g. by using a buffer around the pre-event building outlines. The size of this buffer should depend on the height of the observed building because the debris of a high building may be dispersed further away from the original building contour than the debris of a low building. Moreover, the buffer size has to be determined in relation to the distance to neighbouring buildings since no debris of other buildings may be located within the buffer. This implicates that the buffer size has to be smaller in high-density areas whereas it can be larger in lowdensity areas.

3 International Symposium on Strong Vrancea Earthquakes and Risk Mitigation 459 Figure 1. Compilation of damage types (Schweier and Markus, 2004) Further problems occur at the discrimination of damage types 4a, 4b, 4c and 5, 5a, 5b, 5c, respectively, since it is very difficult to identify which storey collapsed by using exclusively aerial data. As a consequence, these damage types are fused to the damage types pancake collapse of one storey and pancake collapse of more than one storey, respectively. However, since the number of collapsed storeys can only be determined reliably if the height of a floor is known, the discrimination of these two damage types is very fuzzy if it is unknown. Due to the size of the laser footprint and the noise of the data, the different types of debris heaps (7a, 7b, 7c) are also difficult to distinguish. Hence, these damage types are merged as well. Damage type 10 (overhanging elements) cannot be kept apart from unchanged buildings if only aerial data are used. As a result, the following damage types are distinguished in the classification process: a. Unchanged Inclined plane Multi layer collapse Outspread multi layer collapse Pancake collapse of one storey Pancake collapse of more than one storey Heap of debris on uncollapsed storeys Heap of debris Overturn collapse, separated Inclination DATA The test site of this study is a training area of the Swiss Military Disaster Relief (Fig. 2). It is located near Geneve and is used for practicing search and rescue activities. The particularity of this area is that undamaged as well as damaged buildings are located on it. The damaged buildings have different damage types. The size of the area is about 500 m 800 m. Two laser scanning flights were carried out to acquire laser data of the test site. For both flights a TopoSys Falcon II sensor was used. The first flight took place in June 2004, the second one in November During both flights first echo as well as last echo data were recorded. Furthermore, multi spectral information was acquired with a line sensor. Fig. 2 shows an overview of the test site and the buildings and debris, respectively, located on it. The damage types of the marked buildings are listed in Table 1. Moreover, this table includes the size of the undamaged reference buildings.

4 460 M. Rehor & H.-P. Bähr Figure 2. Aerial image of the test site with marked buildings During a pre-processing step, the original point clouds were interpolated into DSMs with 1 m grid size. These DSMs have an accuracy of ±0.15 m in height and ±0.5 m in position. The described damage interpretation method is based on raster data due to the better performance concerning memory access and the well defined neighbourhood. Nevertheless, it has to be mentioned that in principle the technique could also be adapted to point clouds. The acquired LIDAR data contain 3D objects like buildings and vegetation as well as the Earth s surface. In order to extract buildings easier and to remove the influence of the topography, a normalised DSM (ndsm) can be generated by subtracting a digital terrain model (DTM). For the extraction of a DTM from laser scanning data several methods exist (e.g. Lohmann et al., 2000; Sithol and Vosselman, 2004; Tóvári, 2006). In this study the approach of von Hansen and Vögtle (1999) is applied which uses a convex-concave hull (TIN densification). The advantage of an ndsm over a DSM is the simpler data structure, i.e. it contains directly the heights above ground. Therefore, heights extracted from the postevent ndsm can be compared directly with heights of reference buildings. Normally it is a problem to obtain laser scanning data of real damaged buildings. In this study it is exactly the other way round. This means that no laser scanning data of the preevent state are available. For this reason reference models of the undamaged buildings were reconstructed by means of construction plans and aerial as well as terrestrial photographs. Fig. 3 shows the reconstructed reference buildings as grey value coded raster image. In Fig. 4 the last echo ndsm acquired during the first flight over the test area is visualised.

5 International Symposium on Strong Vrancea Earthquakes and Risk Mitigation 461 Figure 3. Pre-event buildings on the test site Figure 4. Last echo post-event ndsm CLASSIFICATION OF BUILDING DAMAGE USING A FUZZY LOGIC APPROACH In this section the workflow of the classification approach is described. It starts with the creation of segments the classification can be based on. Afterwards, features have to be extracted before the segments can be assigned to damage classes by applying a fuzzy logic classification method. Generation of Segments for the Classification Since parameters like the change of inclination of building surfaces and the size of recognisable planes represent two main features for the discrimination of different damage types, planar surfaces have to be extracted from the reference data and the laser scanning data acquired after the disaster in a first step. For the reference models the assumption is made that they are composed of planar surfaces. Hence, the pre-event surfaces can be easily extracted from the reference models (Fig. 5). For the generation of these models different possibilities exist such as photogrammetry or terrestrial measurement. Or they can be reconstructed from construction plans and photographs. Airborne laser scanning data itself is also a suitable technique for the generation of building models (e.g. Steinle, 2005). The post-event planar surfaces (Fig. 6) are derived from the post-event ndsm by means of a region growing algorithm (cf. Steinle, 2005; Rehor and Bähr, 2006). It starts from a seed region fulfilling the condition that the according points are approximately lying in a plane. If a seed region can be found, all neighbouring pixels are tested if they can be assigned to the currently considered plane. For this purpose, statistical tests are used as homogeneity criterion. The plane of best fit is estimated for every segment by least squares adjustment.

6 462 M. Rehor & H.-P. Bähr Figure 5. Planar surfaces of pre-event buildings Figure 6. Planar surfaces extracted from post-event ndsm Since only changes inside the building outlines are analysed during the classification step, the region growing algorithm is only applied on points lying inside a reference building contour plus a buffer of 3 m. In a next step new segments are created on which the classification is based on. For this purpose, the planar surfaces of the reference building models are superposed with the planar surfaces extracted from the post-event data (cf. Fig. 7). This means that each of these new segments corresponds to one of the pre- and one of the post-event planar surfaces. Therefore, parameters like the change of inclination or the height and volume reduction can be calculated for every segment. (a) (b) (c) (d) Figure 7. (a) Planar surfaces of the reference model; (b) planar surfaces extracted from post-event data; (c) superposition of planar surfaces from (a) and (b); (d) new created segments on which the classification is based on Feature Extraction As already mentioned before, features have to be defined and determined for each segment in order to assign them to the damage classes. These features should be selected based on the criterion that they cause a high discrimination between the different classes. In

7 International Symposium on Strong Vrancea Earthquakes and Risk Mitigation 463 consideration of the damage catalogue the following parameters were chosen for the damage classification: Volume reduction Height reduction Change of inclination Size Both volume and height reduction can be defined absolutely or in relation to the pre-event volume or height. The differentiation between these two cases is necessary because a twostoreyed building for instance affected by a pancake collapse of one story has a volume reduction of approximately 50%. In contrary, the relative volume reduction of a skyscraper is very small if only one storey is collapsed although the same damage type occurs. Their absolute volume reductions have in contrary approximately the same size. However, for the determination of other damage types it is important to know the relative values of the parameters. The volume and height reduction can be calculated in the following way: V h red, abs red, abs Vpre Vpost = Vpre Vpost or Vred, rel = 100 % (1) V = h max, pre h max, post pre hmax, pre hmax, post or hred, rel = 100 % (2) h max, pre where V red,abs = absolute volume reduction V red,rel = relative volume reduction V pre = pre-event volume of the segment V post = post-event volume of the segment h red,abs = absolute height reduction h red,rel = relative height reduction h max,pre = maximum pre-event height of the segment h max,post = maximum post-event height of the segment The maximum height of a segment corresponds to its highest point. In this way, it is taken into account that some damage types (e.g. inclined plane, multi layer collapse) have the characteristic that the original building height is preserved in at least one point. In these cases the height reduction is insignificant. The change of inclination is defined as the angle between the normal vectors of the corresponding pre- and post-event planes of the segment. The segment size corresponds to the area of the segment in the ground plane. Classification of the Segments Based on Fuzzy Logic For the classification of building damage a fuzzy logic based technique has been developed. The theory of fuzzy sets was introduced by Zadeh (1965) in order to model uncertainties. While in ordinary Boolean logic an element either belongs to a class or not, fuzzy logic allows the definition of a degree of membership (Tilli, 1993). In the first step of the classification called fuzzyfication membership functions have to be defined for every class and every feature. In this study, they are composed of line segments in order to reduce complexity although in general they do not have to be linear. During fuzzyfication, the a-priori knowledge about the different damage types stored in the damage catalogue is taken into account. This means that the qualitative and quantitative descriptions

8 464 M. Rehor & H.-P. Bähr of the features are converted into membership functions for every damage type. By means of membership functions the degree of membership µ i,j of a segment can be calculated for every parameter j (here j=4 features) and every class i (here i=10 damage types). Afterwards, all membership values µ i,j of a class i have to be combined with an operator. This step is called inference process. It results in a degree of match µ i for every class i. For the combination of the single membership values different operators can be used. Firstly, there are the minimum and the maximum operator which represent the fuzzy generalisation of the logical AND and OR, respectively (Zadeh, 1963). Secondly, the algebraic product can be used which is also a kind of generalisation of the logical AND (Tilli, 1993). Weidner and Lemp (2005) propose the employment of the mean or the median of the single values. In this study these five operators have been tested. However, it has to be pointed out that many more possibilities than those mentioned here exist for the realisation of the inference process. Finally, a decision for one class is made by applying the maximum operator to the degrees of match, i.e. the currently considered segment is assigned to the class i with the largest value µ i. During the segmentation of planar surfaces not all pixels are assigned to segments. Some pixels do not fit in any of the extracted planes and so they remain unsegmented. For these pixels no plane of best fit can be estimated. Since parameters like the inclination change cannot be calculated for them, they have to be treated in a special way during the classification step. As a consequence, the difference of the pre- and post-event height is determined for each of these unsegmented pixels. This height difference h diff is compared with a given threshold t 1 and the pixels are classified as follows: h diff < t 1 : increase h diff > t 1 : reduction h diff < - t 1 : unchanged With respect to the very irregular surface structure of damage types like outspread multi layer collapses or heaps of debris, the assumption can be made that many unsegmented pixels occur in areas affected by such damage types. RESULTS The developed classification approach was applied on the data of the test area. The obtained results are presented in this section. Fig. 5 shows the planar surfaces of the reference models. In Fig. 6 the planar surfaces extracted from the last echo ndsm of the laser scanning flight carried out in June 2004 are visualised. In Fig. 8 (a) the segments resulting from the superposition of the pre- and post-event surfaces are presented. The classification is based on these segments. In Figs. 5, 6 and 8 (a) each segment is displayed in another random colour. The comparison of the results produced by the five different operators tested for the inference process shows the best results for the algebraic product. This confirms the results achieved by Tóvári and Vögtle (2004). In their investigations concerning classification of 3Dobjects in airborne laser scanning data, the product operator also proved to be the one with the best classification rate (see also Tóvári, 2006). Therefore, the results achieved with this operator are visualised in Fig. 8 (b). A detailed overview of these results is given in Table 1. As already mentioned, for each building the real damage type is listed. The colours of the segments correspond to the colours used in Fig. 8 (a). In addition the figure shows how the single segments of the buildings have been classified. Besides, for each segment the degree of membership µ i for the assigned damage class i is given. For the algebraic product µ lies in the interval [0;1]. It

9 International Symposium on Strong Vrancea Earthquakes and Risk Mitigation 465 has to be noted, that the absolute membership value does not represent a probability; therefore, restriction to this value does not allow a statement about the reliability of the segment s classification. On the contrary, the grade of membership has to be put into relation to the second and third largest value. If the difference to these values is large, the decision is relatively reliable. However, in order to give exact statements concerning the reliability of decisions in the classification process, further investigations are necessary. For some selected buildings and segments, respectively, the largest and the second largest degree of membership are compared in Table 2. (a) Figure 8. (a) Segments created by superposing the pre- and post-event planar surfaces; the classification is based on these segments; (b) classification results achieved by the algebraic product for the inference process Additionally, Table 1 contains the size of the single segments, both absolutely and in relation to the area of the reference building. Segments smaller than 3 m 3 m = 9 m 2 are not included in the table. (b)

10 466 M. Rehor & H.-P. Bähr If each building is assigned to that damage type which occupies the relatively largest part of the complete area of the building, the results collected in Table 3 are received. In the column comments it is specified if the building is classified correctly, incorrectly or partially correct. For the meaning of the term partially correct see further explanation in the following paragraphs. Table 1: Classification results achieved by using the algebraic product for the inference Building number Real damage type Area of building in reference data [m²] Colour and number of the segment Classified damage type µ for classified damage type * 100 Area of the segment [m²] Proportion of the total building area [%] a a b a a a a a

11 International Symposium on Strong Vrancea Earthquakes and Risk Mitigation 467 Table 2: Comparison of the highest and the second highest degree of membership for some buildings and segments Building number Real damage type Colour and number of the segment Damage type with largest value µ Largest value µ * 100 Damage type with second largest value µ Second largest value µ * a a Table 3: Compilation of the classification results per building Building number Real damage type Classified damage type Comment correct a 4 partially correct correct correct partially correct 6a 0 0 correct 6b 5 5 correct correct correct 9 3 9a incorrect correct correct correct correct correct incorrect correct It has to be mentioned that the building damage is analysed in much more detail by the described method than by any of the other published approaches. While in our case nine different damage classes are discriminated at the moment, many other approaches distinguish only damaged and undamaged buildings (e.g. Turker and Cetinkaya, 2005; Gusella et al., 2005; Sumer and Turker, 2006). Rezaeian and Grün (2007) differentiate the damaged buildings additionally in totally and partially damaged whereas Chesnel et al. (2007) discriminate between four damage classes according to the European Macroseismic Scale (EMS) (Grünthal, 1998). Referring to Fig. 8 (b) and Table 3, 12 out of the 16 buildings are classified correctly for the most part. If only damaged and undamaged buildings would be discriminated during the classification as in many other approaches, only one building would not have been classified

12 468 M. Rehor & H.-P. Bähr correctly, namely building number 15. The reasons for the misclassifications are discussed in the following: Building number 5 is classified as a pancake collapse of one storey although it is a pancake collapse of more than one storey in reality. This confirms the assumption that the discrimination between these two damage types is very difficult if the height of floors is unknown. A possibility to avoid this complication is the fusion of both damage types. In this case the classification would yield a correct result. This is why building number 5 is described as partially correct in Table 3. The real damage type of building number 2 is a combination of a pancake collapse of more than one storey and an inclination (see Fig. 9, Table 3). It is classified as pancake collapse of one storey. Since no combined damage types are accepted during the classification meaning that every segment can be assigned to only one damage type, the result is satisfactory because as mentioned above the determination of the number of collapsed storeys is very diffuse. Besides, the second largest membership value is the one for the class inclination (see Table 2). Therefore, in further research it must be investigated, whether it is useful to take the damage type with the second largest degree of membership into account during the classification and to accept specific combinations of damage types which occur frequently. Building number 9 (Fig. 10) also belongs to the misclassified buildings. In this case an inclination instead of an outspread multi layer collapse was detected. This happens because the outspread multi layer collapse is a damage type characterized by its extension beyond the original building contour. As in the present status of the approach only the situation within the original area of the building is regarded, parameters like for instance the debris structure outside the footprint cannot contribute to the identification of the damage. In the future, the method will be extended in that way that the situation inside the contour of the reference buildings plus a buffer around these areas is introduced into the classification. Figure 9. Building number 2 Figure 10. Buildings 9 (in front) and 4 (back) Another a priori assumption confirmed by the results is that pixels that are not assigned to planar surfaces during the segmentation process accumulate in areas with particular damage types like for example heaps of debris (e.g. buildings number 7, 8, 10, 11). Most of these pixels show a height reduction. Therefore, it seems to be useful to cluster them and to classify the clusters afterwards. By these procedures, different types of debris heaps may be discriminated. This step should be based on a triangulated surface description instead of planes. The only unchanged building not recognized as such is building number 15 (Fig. 11). The particularity of this building is its barrel-shaped roof which cannot be modelled by planar surfaces. Since the whole approach is based on the comparison of planar surfaces, the region growing algorithm is applied not only to the post-event data but also to the pre-event data of this building. Although the building has not changed, different roof surfaces are extracted for both data sets. By superposing the pre- and post-event surfaces most

13 International Symposium on Strong Vrancea Earthquakes and Risk Mitigation 469 segments show small inclination changes. As a small change of inclination is the only difference between the classes unchanged and inclined plane, it is obvious that the largest part of the building is classified as inclined plane. Table 2 shows that the damage type with the second largest membership value is the class unchanged which corresponds to the real damage type of this building. Furthermore, it is shown in Table 2 that the difference between the two largest degrees of membership is relatively small (0.55 to 0.46). The advantage of the classification method is that different segments belonging to the very same building may be assigned to different classes (e.g. building number 6, Fig. 12). This takes into account that not necessarily the whole building has to be collapsed or damaged after an earthquake. In fact, one single building may be affected by different damage types. The disadvantage, however, is that each segment is analysed separately and that its relations to neighbouring segments remain unconsidered at the moment. This may lead to the result that as for building number 13 a major part of the segments is classified correctly as an identical damage type, whereas some single segments are incorrectly assigned to another damage type. Figure 11. Building number 15 Figure 12. Building number 6 CONCLUSION A new approach for the automatic detection and classification of building damage after disasters like earthquakes was presented. It is based on the comparison of planar surfaces derived from pre- and post-event data. Therefore, it starts with a segmentation of these surfaces, followed by the generation of segments on which the fuzzy logic classification can be applied. In the course of the classification, these segments are assigned to damage types according to there height and volume reduction, their change of inclination, and their size. The results achieved for data of a test area containing real damaged buildings are very promising although there are some aspects that have to be improved in the future. One of them is that only changes inside the pre-event building contour are analysed so far. Thus, further investigations should be carried out to extend the approach in order to include the situation within a buffer around the former building into the analysis. Furthermore, at the moment the segments are classified on their own without taking into account the relations to neighbouring segments. As a consequence, it should be investigated in further research if an improvement can be achieved by considering the damage types of adjacent segments. Besides, the results might be improved if the class with the second largest degree of membership is also regarded. Moreover, particular combinations of damage types appearing frequently should be integrated, e.g. pancake collapses combined with inclinations or heaps of debris on uncollapsed storeys. Moreover, it must be examined how a degree for the reliability of the classification results can be determined. Another aspect requiring further research is the treatment of pixels not assigned to a planar surface in one of the two states. It was pointed out that they concentrate in areas affected by specific damage types. Hence, it should be investigated how they can be clustered and

14 470 M. Rehor & H.-P. Bähr classified to distinguish in more detail between certain damage types like different types of debris heaps. Additionally, in further investigations it will be examined if the results of the classification can be improved by integrating multispectral information as well as terrestrial laser scanning data into the analysis process. It is expected that in this way particular damage types which cannot be detected or determined yet due to the characteristics of airborne laser scanning can then be identified. ACKNOWLEDGEMENTS The presented work has been funded by the Deutsche Forschungsgemeinschaft (DFG) as part of the Collaborative Research Centre (CRC) 461: Strong Earthquakes: A Challenge for Geosciences and Civil Engineering. Furthermore, the authors would like to thank the Swiss Disaster Relief Coordination and Control Centre DDPS for providing their facilities REFERENCES Chesnel, A.-L., Binet, R., Wald, L. (2007): Quantitative Assessment of Building Damage in Urban Area Using Very High Resolution Images. In: Proceedings of the 6 th International Symposium on Remote Sensing of Urban Areas, Paris, France. Gehbauer, F., Markus, M., Engelmann, H., Popa, I., Schweier, C., Rehor, M., Werder, S. (2007): The Disaster Management Tool. In: Proceedings of the International Symposium on Strong Vrancea Earthquakes and Risk Mitigation, October 4-6, Bucharest, Romania. Grünthal, G. (ed.) (1998): European Macroseismic Scale 1998 (EMS-98). Cahiers du Centre Européen de Géodynamique et de Séismologie 15, Centre Européen de Géodynamique et de Séismologie, Luxembourg, 99 pages. Gusella, L., Adams, B. J., Bitelli, G., Huyck, C. K., Mognol, A. (2005): Object-oriented Image Understanding and Post-earthquake Damage Assessment for the 2003 Bam, Iran, Earthquake. Earthquake Spectra, Vol. 21, No. S1, pp von Hansen, W., Vögtle, T. (1999): Extraktion der Geländeoberfläche aus flugzeuggetragenen Laserscanner-Aufnahmen. Photogrammetrie Fernerkundung Geoinformation (PFG), Nr. 4/1999, pp Lohmann, P., Koch, A., Schaeffer, M. (2000): Approaches to the Filtering of Laser Scanner Data. In: International Archives of Photogrammetry and Remote Sensing, Amsterdam, The Netherlands, Vol. XXXIII, Part B3, pp Murakami, H., Nakagawa, K., Shibata, T., Iwanami, E. (1998): Potential of an airborne laser scanner system for change detection of urban features and orthoimage development. In: International Archives of Photogrammetry and Remote Sensing, Stuttgart, Germany, Vol. XXXII, Part 4, pp Rehor, M., Bähr, H.-P. (2006): Segmentation of damaged buildings from laser scanning data. In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, September 20-22, Bonn, Germany, Vol. XXXVI, Part 3, ISSN , pp Rehor, M. (2007): Classification of building damages based on laser scanning data. In: Proceedings of the ISPRS Workshop Laser Scanning 2007, September 12-14, Espoo, Finland. Rezaeian, M., Grün, A. (2007): Automatic classification of collapsed buildings using object and image space features. In: Li, J., Zlatanova, S., Fabbri, A. (eds.): Geomatics Solutions for Disaster Management, Lecture Notes in Geoinformatics and Cartography, Springer, Berlin, Germany. Schweier, C., Markus, M. (2004): Assessment of the search and rescue demand for individual buildings. In: Proceedings of the 13th World Conference on Earthquake Engineering, Vancouver, Canada.

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