Document Type : Research Paper


1 MSc in Photogrammetry, School of Surveying and Geospatial Engineering, University of Tehran, College of Engineering

2 Assistant Professor, School of Surveying and Geospatial Engineering, University of Tehran, College of Engineering


Extended abstract
Given the population growth and increasing urbanization, the occurrence of natural disasters like earthquake can cause heavy losses and damages and interrupt the development of cities and countries. Among these disasters, the earthquakeisof great importance due to its unpredictability and high frequency in relation to other events, as well as its location on the earthquake belt. According to the last year's estimate, Iran has been one of the 6 countries with high mortality rates in earthquakes. Therefore, finding a way to minimize the losses can be critical. Crisis managers need quick information from the affected area after the earthquake to minimize the fatalities and financiallosses. The destruction map is one of the information that helps crisis managers. These maps show the destructed buildings or roads with their degree of destruction. With these maps, the destructed buildings and roads can be found quickly.
Materials & Methods
Many methods are used to prepare the destruction maps, such as aerial/satellite images, LiDAR data, etc. These information can be used to determine the destructed buildings automatically or by visual interpretation. Visual interpretation for determining the degree of destruction requires operator. Although this method has high accuracy, it is less considered because it is time consuming and needs specialists to interpret the data. Therefore, researchers have focused on automated processing techniques for the production of the destruction maps. Various automatic change detection techniques are used to evaluate the destruction resulting from earthquakeby comparing satellite images in two pre and post-earthquake periods based on satellite and aerial images. LiDARdata is one of the most important sources of information to determine destructed buildings with high accuracy and speed. LiDAR data provides the possibility of 3-D demonstration of the destructed region. This information is a great help in preparing the destruction map automatically. The recent expansion of the LIDAR technology is due to the high spatial power of these data. As a result, many researchers have focused on developing an automatic destruction map using Lidardata.Although considering the textural information from the Lidar data, like homogeneity in the destructed region can be effective in distinguishing between the destroyedand undestroyed buildings.
In this paper, a new algorithm is proposed to prepare the destruction map after the earthquake by integratingthe post-event high resolution satellite images and post-event LiDAR data. In the proposed method, different textural descriptors of the LiDAR image and data are extractedafter the necessary preprocessing on the satellite image andLiDAR data after the earthquake. In the next step, using the layer of buildings extracted from the map,the areas of the buildings are extracted from the satellite image and LiDAR data, as well as the satellite image descriptors and LiDARdata.Then, the textural descriptorsextracted from the satellite image and LiDAR data are combinedtogether. After that, the points inside this area are categorized into two classes of "debris" and "intact" by the method of support vector machine. Finally, based on the area of the debris class of each building, destroyed and undestroyed buildings were identified by taking a threshold limit into consideration. This algorithm is executed on each building from the destruction part to produce the final destruction map
In order to evaluate the proposed method,the data set was selected from the city of Port-au-Prince, the capital of Haiti, after the 2010 earthquake. According to the USGS reports, 97,294 buildings were damaged and 188,383 were destroyed in Port-au-Prince and most of the southern parts of Haiti.  Furthermore, reports show that 222,570 people were killed, 300,000 were injured, and 1.3 million people were displaced. The sample data set include post-event WorldView II satellite images as well as post-event LiDAR data. The WorldView II satellite took images on January, 16 2010, and the LiDAR date was also obtained from this topography website. Obtaining LiDAR data is from January, 21 2010 to January, 27 2010. The vector map of the selected test area was generated in ArcGIS environment. By evaluating the proposed method and using the existing data, the overall accuracy of 97% and the Kappa coefficient of 92% were obtained which proved the reliability of this technique.
In this paper, a new method for the generation of damage map based on the integration of high resolution satellite images and LiDAR data was proposed. The results show the ability of this method in generating destruction maps based on the satellite images with high resolution and LiDAR data. In comparing similar studies, the results are satisfactory. The selection of the appropriate descriptors, correct training data, the elimination of non-building areas from the sample data, the integration of satellite images and LiDARdate can be known as the reason behind obtaining these results.


1. جانعلی‌پور،م.،محمدزاده،ع. 1393. تعیین میزان تخریب ساختمان ها پس از زلزله با به کارگیری مدل ANFIS و تصاویر سنجش از دوری. دوفصلنامه علمی پژوهشی مدیریت بحران، 7: 79-91.
2. رنجبر،ح.،اردلان،ع.،دهقانی،ح.،سراجیان،م.،علیدوستی،ع. 1393. تسهیل فاز واکنش مدیریت بحران زلزله با استخراج خودکارساختمان ها برمبنای آنالیز بافت از تصاویر ماهواره‌ای. دوفصلنامه علمی پژوهشی مدیریت بحران، 5: 5-19.
3. رنجبر،ح.،اردلان،ع.،دهقانی،ح.،سراجیان،م.،علیدوستی،ع. 1393. ارزیابی روش‌­های استخراج اطلاعات فیزیکی ساختمان‌های تخریب شده ناشی از زلزله و ارائه ­الگوریتمی بر پایه لایه­‌هایGIS و سنجش از دور. فصلنامه علمی پژوهشی اطلاعات جغرافیایی، 23(91): 21-42.
4. Das, S., Srivastava, A. N., &Chattopadhyay, A. (2007). Classification Of Damage Signatures In Composite Plates Using One-Class Svms. Paper Presented At The Aerospace Conference, 2007 Ieee.
 5. Dong, L., & Shan, J. (2013). A Comprehensive Review Of Earthquake-Induced Building Damage Detection With Remote Sensing Techniques. Isprs Journal Of Photogrammetry And Remote Sensing, 84, 85-99.
6. Gamba, P., &Casciati, F. (1998). GisAnd Image Understanding For Near-Real-Time Earthquake Damage Assessment. Photogrammetric Engineering And Remote Sensing, 64, 987-994.
7. Guo, H., Liu, L., Lei, L., Wu, Y., Li, L., Zhang, B., Et Al. (2010). Dynamic Analysis Of The Wenchuan Earthquake Disaster And Reconstruction With 3-Year Remote Sensing Data. International Journal Of Digital Earth, 3(4), 355-364.
8. Joachims, T. (2002). Introduction To Support Vector Machines: Cambridge University Press, Cambridge.
9. Labiak, R. C., Van Aardt, J. A. N., Bespalov, D., Eychner, D., Wirch, E., &Bischof, H.-P. (2011). Automated Method For Detection And Quantification Of Building Damage And Debris Using Post-Disaster Lidar Data. Paper Presented At TheSpieDefense, Security, And Sensing.
 10. Li, M., Cheng, L., Gong, J., Liu, Y., Chen, Z., Li, F., Et Al. (2008). Post-Earthquake Assessment Of Building Damage Degree Using Lidar Data And Imagery. Science In China Series E: Technological Sciences, 51(2), 133-143.
 11. Maruyama, Y., Tashiro, A., & Yamazaki, F. (2011). Use Of Digital Surface Model Constructed From Digital Aerial Images To Detect Collapsed Buildings During Earthquake. Procedia Engineering, 14, 552-558.
 12. Menderes, A., Erener, A., &Sarp, G. L. (2015). Automatic Detection Of Damaged Buildings After Earthquake Hazard By Using Remote Sensing And Information Technologies. Procedia Earth And Planetary Science, 15, 257-262.
 13. Rastiveis, H., Eslamizade, F., &Hosseini-Zirdoo, E. (2015). Building Damage Assessment After Earthquake Using Post-Event Lidar Data. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., Xl-1-W5, 595-600.
14. Rastiveis, H., Samadzadegan, F., &Reinartz, P. (2013). A Fuzzy Decision Making System For Building Damage Map Creation Using High Resolution Satellite Imagery. Natural Hazards And Earth System Sciences, 13(2), 455.
15. Rehor, M., Bahr, H. P., TarshaKurdi, F., Landes, T., &Grussenmeyer, P. (2008). Contribution Of Two Plane Detection Algorithms To Recognition Of Intact And Damaged Buildings In Lidar Data. The Photogrammetric Record, 23(124), 441-456.
16. Samadzadegan, F., ValadanZoj, M. J. V., &Moghaddam, M. K. (2010). Fusion OfGis Data And High-Resolution Satellite Imagery For Post-Earthquake Building Damage Assessment.
17. Sarp, G., Erener, A., Duzgun, S., &Sahin, K. (2014). An Approach For Detection Of Buildings And Changes In Buildings Using Orthophotos And Point Clouds: A Case Study Of Van Erciÿ Earthquake. European Journal Of Remote Sensing-2014, 47, 627-642.
18. Tian, J., Nielsen, A. A., &Reinartz, P. (2015). Building Damage Assessment After The Earthquake In Haiti Using Two Post-Event Satellite Stereo Imagery And Dsms. International Journal Of Image And Data Fusion(Ahead-Of-Print), 1-15.
19. Tong, X., Hong, Z., Liu, S., Zhang, X., Xie, H., Li, Z., Et Al. (2012). Building-Damage Detection Using Pre-And Post-Seismic High-Resolution Satellite Stereo Imagery: A Case Study Of The May 2008 Wenchuan Earthquake. Isprs Journal Of Photogrammetry And Remote Sensing, 68, 13-27.
20. Trinder, J., & Salah, M. (2012). Aerial Images AndLidar Data Fusion For Disaster Change Detection. Isprs Annals Of Photogrammetry, Remote Sensing And Spatial Information Sciences, 1, 227-232.
21. Turker, M., &Cetinkaya, B. (2005). Automatic Detection Of Earthquake Damaged Buildings Using Dems Created From Pre And Post Earthquake Stereo Aerial Photographs. International Journal Of Remote Sensing, 26(4), 823-832.
22. Usgs. (2011). Post- Disaster Building Damage Assessment Using Satellite And Aerial Imagery Interpretation, Field Verification And Modeling Techniques.
23. S.O. Hashemi-Parast, F. Yamazaki, And M. Estrada. (2016). Monitoring And Evaluation Of The Urban Reconstruction Process In Bam, Iran, After The 2003 Mv 6.6 Earthquake, Natural Hazards, Pp. 1-17.