CLASSIFICATION OF BUILDING DAMAGE BASED ON LASER SCANNING DATA. Miriam Rehor

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1 The Photogrammetric Journal of Finland, 20(2), 2007 Received , Accepted CLASSIFICATION OF BUILDING DAMAGE BASED ON LASER SCANNING DATA Miriam Rehor Institute of Photogrammetry and Remote Sensing (IPF), Universität Karlsruhe (TH), Germany ABSTRACT Airborne laser scanning (ALS) systems allow a fast and extensive acquisition of precise height data. Therefore, they are especially suitable for obtaining large scale information about the damage situation directly after disasters. In this article a detection and classification method for damage occurring on buildings in affected areas is presented. The technique is based on the comparison of planar roof planes from pre-event building models and planar surfaces extracted from laser scanning data acquired immediately after the disaster. These pre- and post-event surfaces are geometrically overlaid and the geometric changes of corresponding planes are analysed. Therefore, features like volume and height reduction, change of inclination or size can be determined for each pair of corresponding planes. Finally, a segment-based fuzzy logic classification is carried out using these features. The results achieved by applying this technique on data of a real test area containing damaged buildings as well as on simulated data are presented and analysed. 1. INTRODUCTION Every year many people die because buildings collapse due to disasters like earthquakes. The resources needed to free people trapped in collapsed buildings depend amongst others on the damage types of the affected buildings (Schweier and Markus, 2004). Therefore, a fast and extensive damage analysis answering not only the question which buildings collapsed but also how they are damaged could help to employ the available resources in an optimal way to save as many lives as possible. Hence, one project of the German Collaborative Research Centre (CRC) 461 Strong Earthquakes: A Challenge for Geosciences and Civil Engineering works on the development of an automatic building damage detection and classification method. This technique presented in this article is based on the comparison of planar roof planes of pre-event building models and planar surfaces extracted from ALS data acquired directly after the disaster. ALS is used due to the possibility of acquiring precise height data of large areas rapidly without having to enter affected areas. The classification step is realised by a segment-based fuzzy logic approach. At the current state, however, only the situation inside the contour of buildings contained in the pre-event data set is taken into account. The method was tested on data of a real test area containing buildings with different damage types as well as on simulated data. The obtained results are presented and analysed. 2. RELATED WORK The use of ALS data for building damage detection after disasters has already been suggested several times, 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 advantage of using ALS data is the integration of the height component into the analysis. 54

2 Therefore, damage types which cannot be identified in 2D data (e.g. pancake collapses) can be detected. But there is also a problem concerning the use of ALS data as basis of damage detection methods, namely the current difficulty to get such data acquired immediately after a disaster for developing and testing approaches. As a consequence, the original application of the methods mentioned above was the detection of changes in urban areas. Until now they have never been tested on data containing real building damage. Another possibility to include the height component is the use of digital surface models (DSM) generated from aerial stereo images which are already available for areas affected by disasters (see Turker and Cetinkaya, 2005; Rezaeian and Grün, 2007). The drawback of these DSMs is the lower accuracy of their height component which is according to Turker and Cetinkaya (2005) or Rezaeian and Grün (2007) approximately ten times worse than the height component of DSMs derived from ALS data. Most of the techniques mentioned in the previous paragraph discriminate only undamaged and damaged buildings (e.g. Turker and Cetinkaya, 2005; Gusella et al, 2005; Sumer and Turker, 2006). Rezaeian and Grün (2006) distinguish between totally damaged, partially damaged and undamaged buildings, whereas Chesnel et al. (2007) differentiate between four damage classes according to the European Macroseismic Scale (EMS) (Grünthal, 1998). Hence, none of these approaches distinguishes between different damage types as it is proposed by Schweier and Markus (2004, 2006). They developed a so-called damage catalogue containing different damage types of entire buildings after earthquakes (Figure 1). For each damage type this damage catalogue consists of a description and some geometrical features like volume and height reduction, the change of the inclination of building surfaces as well as the surface structure and the size of the recognisable planes. Pictures of more than 100 damaged buildings have been analysed in order to determine qualitative and quantitative values for these features. For expressing qualitative information linguistic terms are used; a multi layer collapse has e.g. a small volume reduction. An example for quantitative information defined by numerical values is the volume reduction of 60 % - 80 % for a heap of debris. During the development of the damage catalogue the special characteristics of aerial data acquisition were taken into account. This means that attention was paid to the fact that the geometrical features characterising each damage type can be derived from aerial data (e.g. ALS). Furthermore, it was kept in mind that rescue activities can be supported by the knowledge of these features. Figure 1. Compilation of damage types (Schweier and Markus, 2004) If ALS data are used for object extraction or classification applications, it is often advantageous to generate a normalised DSM (ndsm) because it contains only the 3D objects on the Earth s surface like buildings and vegetation. It can be derived from the DSM by subtracting a digital terrain model (DTM) (e.g. Oude Elberink and Maas, 2000; Steinle and Vögtle, 2001). In this 55

3 study the approach of von Hansen and Vögtle (1999) which uses a convex-concave hull (TIN densification) is used for DTM generation from ALS data. For the extraction of planar surfaces from DSMs or ndsms Steinle (2005) developed a region growing algorithm which starts from a seed region. The points belonging to this seed region have to lie approximately in a plane. For testing the affiliation of a neighbouring pixel to the currently considered plane, a global test and a test for blunders in a Gauss-Markov model are used as homogeneity criterion (Rehor and Bähr, 2006). The plane of best fit is estimated by least squares adjustment for each detected segment. At the beginning of a segment-based fuzzy logic classification membership functions have to be defined for every class and every feature (fuzzification). With these membership functions a degree of membership μ i,j can be determined for each segment with every feature j according to every class i. In the inference process the j degrees of membership for every class i have to be combined. For this combination different operators can be used, e.g. the minimum and maximum operator (Zadeh, 1965), the algebraic product (Tilli, 1993), the mean or the median of the single values (Weidner and Lemp, 2005). The inference process results in a degree of match μ i for every class i. Finally, the segment is assigned to that class i with the highest degree of match μ i. Tóvári and Vögtle (2004) tested different operators for the inference process (minimum, maximum, weighted sum, algebraic product) in their investigations concerning the classification of 3D objects in ALS data. They concluded that the results with the highest classification rate were provided by the product operator. 3. DAMAGE TYPES The damage types discriminated in the classification method described in this article are based on the damage catalogue of Schweier and Markus (2006) (see section 2). Only small changes were carried out. So, the different types of pancake collapses of one storey (4a, 4b, 4c) and pancake collapses of more than one storey (5, 5a, 5b, 5c) respectively were merged to one damage type in each time because it seems to be impossible to identify which storey collapsed if aerial data are used exclusively. The different types of debris heaps were fused as well because their discrimination has proved to be very difficult. At the current state of the method only changes within the reference building contours are analysed. Therefore, features like debris structure outside the footprint cannot contribute to the discrimination of the various damage types. As a consequence, damage types characterised very well by these features (e.g. overturn collapse) may not yet be identified within the current classification procedure. Hence, this procedure should be extended in future, e.g. by using a buffer around the pre-event building contour. Since overhanging elements cannot be detected by using aerial data exclusively, the following damage types can be distinguished in the classification process: 0. Unchanged 1. Inclined plane 2. Multi layer collapse 3. Outspread multi layer collapse 4. Pancake collapse of one storey 5. Pancake collapse of more than one storey 6. Heap of debris on uncollapsed storeys 7. Heap of debris 8. Overturn collapse, separated 9a. Inclination 56

4 4. DATA 4.1 Real data of a test area containing damaged buildings The test site of this study contains undamaged buildings as well as buildings with different damage types. It is situated near Geneva and has an extension of about 500 m 800 m (Figure 2). The area is owned by the Swiss Military Disaster Relief and is used for training search and rescue activities. The damage types of the buildings marked in Figure 2 are composed in Table 1. ALS data of the area were acquired in 2004 with a TopoSys Falcon II sensor. The original point clouds were transformed into DSMs with 1 m raster width having an accuracy of about ± 0.5 m in position and ± 0.15 m in height. Raster data are used as basis for the approach because of the well defined neighbourhood and the better performance concerning memory access. But it has to be mentioned that in principle this technique can also be adapted to point clouds. 3D models of the undamaged buildings were reconstructed by means of construction plans and photographs. 4.2 Simulated data As mentioned in section 2, it is a problem so far to get ALS data containing a significant amount of damaged buildings for developing building damaged classification techniques. Therefore, a software was developed at the Institute of Photogrammetry and Remote Sensing (IPF, Universität Karlsruhe (TH), Germany) which allows to simulate a laser scanner flight based on CAD models in order to get ALS data of additional buildings with different damage types for testing the described method. Schweier et al. (2004) describe how 3D CAD models of damaged buildings can be created from digital photographs of collapsed buildings acquired after disasters using photogrammetric evaluation. As pre-event building models are needed as reference for the method described in this article, the initial undamaged state of these buildings has to be reconstructed from on-site investigations. For the pre-event state no ALS data have to be simulated. The knowledge of the roof planes of the reference building models is rather enough. Figure 3 (a) shows an example for a CAD model of an undamaged building created in this way which will be referred to as building number 17 in the rest of the article. Figure 3 (b) shows the respective CAD model of the post-event state. A photograph of the damaged building is presented in Figure 3 (c). 5. CLASSIFICATION OF BUILDING DAMAGE This section presents the whole workflow of this building damage classification approach. As input data the planar roof planes of pre-event building models as well as a post-event ndsm are used. The pre-event building models can be created by using e.g. ALS data, photogrammetry, terrestrial measurements or construction plans. Concerning the usability of the method in real cases it might be a restriction that it only works if pre-event building models are available. But in times of growing importance of 3D city models such models will exist for more and more cities in future. If no pre-event data are available for the affected area only assumptions about the damage situation can be made (see Rehor and Bähr, 2006). As two main features characterising different damage types are the size of recognisable planes and the change of inclination of building surfaces (see section 2), planar surfaces are extracted from the post-event ndsm with the region growing algorithm described in section 2. Afterwards, the pre- and post-event planar surfaces are geometrically overlaid and segments are created in this 57

5 (a) CAD model of the pre-event state (b) CAD model of the post-event state Figure 2. Aerial image of the test site with marked buildings (c) Photograph of the damaged building Figure 3. Example of a damaged building for which pre- and post-event CAD models were reconstructed way that each segment corresponds to one pre- and one post-event planar surface. The characteristics of the corresponding pre- and post-event planes can then be compared for each of these segments in a next step. The features used for assigning the segments to the different damage types (cf. section 3) have to cause a high discrimination between the different classes. Hence, the expert knowledge about the damage types compiled in the damage catalogue (cf. section 2) was analysed and the following features were chosen in order to maximize the discrimination: - Volume reduction - Height reduction - Change of inclination - Size The volume reduction is defined as ratio of the difference between the segment s pre- and postevent volume and its pre-event volume. The ratio of the difference between the maximum preevent and the maximum post-event height of the segment and the maximum pre-event height represents the height reduction. The change of inclination is defined as angle between the normal vectors of the corresponding pre- and post-event planes. The size corresponds to the area covered by the segment in the ground plane. For the fuzzy logic classification membership functions have to be defined (cf. section 2). These membership functions are composed of linear segments in this study in order to simplify matters. During this step, the a priori expert knowledge about the damage types composed in the damage catalogue is taken into account (see section 2). In this process, the qualitative and quantitative descriptions of the features are converted into membership functions for every damage type. Two examples for membership functions for the feature change of inclination are visualised in Figure 4. 58

6 Figure 4. Membership functions of the feature change of inclination for pancake collapse and inclined plane During the extraction of planar surfaces not necessarily all pixels are assigned to segments. There are also pixels which remain unsegmented because they do not fit to any of the extracted planes. As no plane of best fit can be estimated for these pixels, the change of inclination cannot be calculated. Therefore, these pixels are not included into the classification process of the segments but treated separately. So the difference of the pre- and post-event height is determined for each of these unsegmented pixels. This height difference h diff is analysed by comparing it to a threshold t determined empirically from the data of the test site and is classified as unchanged, height reduction and height increase (Rehor, 2007; Rehor and Bähr, 2007). 6. RESULTS In the following the results obtained by applying the whole approach on the data of the test site as well as on the simulated data are presented. Figures showing the pre- and post-event planar surfaces and the segments on which the classification is based on are not visualised in this article. For the data of the training area they are presented in Rehor (2007) and Rehor and Bähr (2007). The comparison of the results achieved with the five different operators (minimum, maximum, algebraic product, median, mean) for the inference process shows the highest classification rate for the algebraic product. This means that the product operator provides most correctly classified building parts what verifies the achievements of Tóvári and Vögtle (2004) (section 2). Therefore, the results obtained by the algebraic product are shown in Figure 5 and analysed in the following. For a better overview of the results each building was assigned to that damage type which occupies the relatively largest part of its complete area. These damage types are compared to the real damage types of the buildings in Table 1. The column comments specifies if the building is classified correctly, incorrectly or partially correct. The meaning of the term partially correct is explained later on. Referring to Table 1, 12 out of 16 buildings are classified correctly for the most part. If only damaged and undamaged buildings were discriminated during the classification process as in many other approaches (cf. section 2), only one building would have been classified incorrectly, namely building 15. The reasons for the occurring misclassifications are discussed in the following. The real damage type of building 5 is a pancake collapse of more than one storey but it is classified as pancake collapse of one storey. If the height between floors is not known and therefore not introduced into the classification as additional knowledge, the discrimination between these two damage types is very difficult and fuzzy. But if these damage types would be merged to the more general damage type pancake collapse, building 5 would be classified correctly. Therefore, building 5 is specified as partially correct in Table 1. For building 2 a similar case occurs. The real damage type of this building is a combination of a pancake collapse of more than one storey and an inclination but it is classified as pancake collapse of one storey. So the general solution pancake collapse would also be acceptable because each segment can be 59

7 assigned to only one damage type. In future it should be investigated how particular combinations of damage types occurring frequently can be integrated into the classification process. Figure 5. Classification results achieved with the algebraic product for the inference process. The buildings are numbered according to Figure 2. Building ID Real Classified damage type 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 partially correct Table 1. Classification results and real damage types of the buildings marked in Figure 2 One of the two totally misclassified buildings is building 9. The reason for this misclassification is the restriction of the analysis on changes inside the reference building areas. So the outspread multi layer collapse of this building which is characterised by the extension of debris beyond the reference building contour is identified as inclination because the analysis of changes in the area around the former building contour is not yet included into the method. The second misclassified building is building 15. It represents an exception because of its barrelshaped roof which cannot be composed by planar surfaces. In order to apply the classification 60

8 method based on the comparison of planar surfaces on this building, its roof was approximated by planar surfaces. But although building 15 did not change, the planar surfaces extracted from the post-event ndsm using the region growing algorithm (cf. section 2, 4) are not identical with the pre-event surfaces. And as the change of inclination of corresponding pre- and post-event planes is significantly larger than zero, the large part of the building is not classified as unchanged. It is assigned to the damage type inclined plane instead, because the only difference between the damage types 0 and 1 is the change of inclination which is a bit larger for inclined planes. Building 17 for which laser scanning data were generated synthetically is only partially damaged. As it can be seen in Figure 3 (b) and (c) it has a multi layer collapse which cannot be detected by using aerial data exclusively because the snapped off parts of the roof plane are almost vertical. Since the method cannot recognise such a phenomenon these building parts were classified as unchanged and as heaps of debris. Only one small part of the building is identified as heap of debris on uncollapsed storeys (cf. Figure 5). Due to the irregular surface of debris heaps the assumption can be made that many unsegmented pixels showing a height reduction (section 5) accumulate in areas with this damage type. This is confirmed by Figure 5 in conjunction with Table 1 and Figure 2 for the buildings 7, 8, 10 and CONCLUSION The results obtained with the presented method for building damage detection and classification after disasters are very promising. The described method starts with the extraction of planar surfaces from pre- and post-event data. These surfaces are geometrically overlaid and segments are created which are assigned to damage types using a fuzzy logic classification. At the current state of the approach only changes inside the outlines of reference buildings are analysed. Therefore, this method should be extended in further research in order to include the situation around the contours of the reference buildings into the analysis. Within the described technique, each building is divided into several segments by overlaying the pre- and post-event planar surfaces. Each of these segments is classified on its own during the classification process. As a consequence, different segments belonging to the same building may be assigned to different damage classes. This has the advantage that different damage types can be detected in different parts of one building (e.g. buildings 6 and 17, Figure 5). But there is also the possibility that most segments belonging to a building which has only one damage type are classified correctly as the same damage type whereas some small segments are misclassified (e.g. building 13). In such a case the whole building should be assigned to the damage type determined for the bulk of the building. This shows that further investigations should be carried out in order to improve the method by considering the damage types of adjacent segments. Furthermore, particular combinations of damage types appearing frequently should be integrated, e.g. pancake collapses combined with inclinations. Moreover, it has to be investigated how a degree of reliability can be determined for the classification results. The treatment of pixels not fitting to any of the extracted planar surfaces and therefore remaining unsegmented needs also further research. It was pointed out that such pixels often accumulate in areas affected by heaps of debris. Hence, it should be examined if different types of debris heaps can be distinguished by clustering these pixels and classifying these clusters afterwards. The integration of spectral information into the analysis might also improve the discrimination of these damage types. 61

9 ACKNOWLEDGEMENT This study was carried out in the Collaborative Research Center (CRC) 461 Strong Earthquakes: A Challenge for Geosciences and Civil Engineering at the Universität Karlsruhe (TH), which is funded by the Deutsche Forschungsgemeinschaft (German Research Foundation) and supported by the state of Baden-Württemberg and the Universität Karlsruhe (TH). REFERENCES Chesnel, A.-L., Binet, R., Wald, L., Quantitative Assessment of Building Damage in Urban Area Using Very High Resolution Images, Urban Remote Sensing Joint Event 2007, Paris, France, 5 pages, &arnumber= ( ). Grünthal, G. (ed.), 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., Object-oriented Image Understanding and Post-earthquake Damage Assessment for the 2003 Bam, Iran, Earthquake. Earthquake Spectra, Vol. 21(S1), pp von Hansen, W., Vögtle, T., Extraktion der Geländeoberfläche aus flugzeuggetragenen Laserscanner-Aufnahmen. Photogrammetrie Fernerkundung Geoinformation, Nr. 4/1999, pp Murakami, H., Nakagawa, K., Shibata, T., Iwanami, E., Potential of an airborne laser scanner system for change detection of urban features and orthoimage development, International Archives of Photogrammetry and Remote Sensing, Stuttgart, Germany, Vol. 32, Part 4, pp Oude Elberink, S., Maas, H.-G., The use of anisotropic height texture measures for the segmentation of laserscanner data, International Archives of Photogrammetry and Remote Sensing, Amsterdam, The Netherlands, Vol. 33, Part B3/2, pp Rehor, M., Classification of building damages based on laser scanning data, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 36, Part 3/W52, pp Rehor, M., Bähr, H.-P., Detection and analysis of building damage caused by earthquakes using laser scanning data, Proceedings of International Symposium on Strong Vrancea Earthquakes and Risk Mitigation, Bucharest, Romania, pp Rehor, M., Bähr, H.-P., Segmentation of damaged buildings from laser scanning data, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Bonn, Germany, Vol. 36, Part 3, pp Rezaeian, M., Grün, A., Automatic classification of collapsed buildings using object and image space features, in J. Li, S. Zlatanova, A. Fabbri, eds., Geomatics Solutions for Disaster Management, Lecture Notes in Geoinformatics and Cartography, Springer, Berlin, Germany, pp

10 Schweier, C., Markus, M., Classification of collapsed buildings for fast damage and loss assessment. Bulletin of Earthquake Engineering, Vol. 4, No. 2, pp Schweier, C., Markus, M., Assessment of the search and rescue demand for individual buildings, Proceedings of the 13th World Conference on Earthquake Engineering, Vancouver, Canada, Paper No. 3092, WCEE_2004_SARdemand.pdf ( ). Schweier, C., Markus, M., Steinle, E., Simulation of earthquake caused building damages fort he development of fast reconnaissance techniques. Natural Hazards and Earth System Sciences, Vol. 4, No. 2, pp Steinle, E., Gebäudemodellierung und -änderungserkennung aus multitemporalen Laserscanningdaten, Dissertation, Deutsche Geodätische Kommission, Reihe C, Heft Nr. 594, Verlag der Bayerischen Akademie der Wissenschaften, München, pp , ( ). Steinle, E., Vögtle, T., Automated extraction and recognition of buildings in laserscanning data for disaster management, in E.P. Baltsavias, A. Gruen and L. Van Gool, eds., Automatic Extraction of Man-Made Objects from Aerial and Space Images (III), Swets & Zeitlinger, Lisse, The Netherlands, pp Sumer, E., Turker, M., An integrated earthquake damage detection system, Proceedings of the 1 st International Conference on Object-based Image Analysis (OBIA 2006), Salzburg, Austria, %20-%20Geology,%20Soil,%20Natural%20Resources/OBIA2006_Sumer_Turker.pdf ( ). Tilli, T., Mustererkennung mit Fuzzy-Logik, Franzis-Verlag GmbH, München, 336 p. Tóvári, D., Vögtle, T., Classification methods for 3D objects in laserscanning data, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Istanbul, Turkey, Vol. 35, Part B3, pp Turker, M., Cetinkaya, B., Automatic detection of earthquake-damaged buildings using DEMs created from pre- and post-event stereo aerial photographs. International Journal of Remote Sensing, Vol. 26, No. 4, pp Vögtle, T., Steinle, E., Detection and recognition of changes in building geometry derived from multitemporal laserscanning data, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Istanbul, Turkey, Vol. 35, Part B2, pp Vu, T.T., Matsuoka, M., Yamazaki, F., LIDAR-based Change Detection of Buildings in Dense Urban Area. Proceedings of the International Geoscience and Remote Sensing Symposium, IEEE, Anchorage, Alaska, USA, CD-ROM, pp Weidner, U., Lemp, D., Objektorientierte Klassifizierung, in H.-P. Bähr, T. Vögtle, eds., Digitale Bildverarbeitung Anwendungen in Photogrammetrie, Fernerkundung und GIS, 4. edn., Wichmann Verlag, Heidelberg, pp Zadeh, L.A., Fuzzy Sets. Information and Control, Vol. 8, pp