A Hybrid Approach Integrating 3D City Models, Remotely Sensed SAR Data and Interval-Valued Fuzzy Soft Set Based Decision Making for Post Disaster Mapping of Urban Areas

  • Iftikhar AliEmail author
  • Aftab Ahmed Khan
  • Salman Qureshi
  • Mudassar Umar
  • Dagmar Haase
  • Ihab Hijazi
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


This chapter introduces a novel technique for post-disaster mapping and disaster scale estimation based on an integrated framework of SAR remote sensing and a 3D city model database, optical remote sensing imagery is used for validation purpose. SAR based urban damage detection is well established and has been used for many years. We have showed that how the existing 3D City Model can be updated with the information extracted from satellite remote sensing data. The third dimension will play a very crucial role in evacuation rout planning of damaged or affected buildings. In this study in the proposed–three-level (L1, L2, L3)–model damage assessment information is integrated with the semantic knowledge of 3D city models in order to better organize the search and rescue operation. L1 includes remotely sensed Synthetic Aperture Radar (SAR) space-borne data collection from the affected areas; L2 includes a change detection process; and L3 includes the estimation of the most affected building(s). Using this model, we show how the day-night image acquisition capability of a SAR sensor and semantic information from a 3D city model can be effectively used for post disaster mapping for rapid search and rescue operations. For L1, the combination of very high resolution SAR data and a 3D city model in CityGML format is used. L2 works under predefined conditions to detect the types of changes that have occurred. In L3, an interval-valued fuzzy soft set theory method is proposed with which to estimate the scale of damage to the affected structures (buildings). In this study, we show the potential application of existing 3D city models (with semantic knowledge) in combination with SAR remote sensing for post disaster management activities, especially for search and rescue operations.


Urban remote sensing Change detection CityGML Hazard mapping Spatial analysis 



The authors would like to thank DigitalGlobe, Astrium Services, and USGS for providing the remote sensing data used in this study and the IEEE GRSS Data Fusion Technical Committee for organizing the Data Fusion Contest.


  1. Brunner D (2009) Advanced methods for building information extraction from very high resolution SAR data to support emergency response. Ph.D., University of Trento, Trento, ItalyGoogle Scholar
  2. Brunner D, Lemoine G, Bruzzone L (2010) Earthquake damage assessment of buildings using VHR optical and SAR imagery. IEEE Trans Geosci Remote Sens 48:2403–2420. doi: 10.1109/TGRS.2009.2038274 CrossRefGoogle Scholar
  3. Chetia B, Das PK (2010) An application of interval-valued fuzzy soft sets in medical diagnosis. Int J Contemp Math Sci 5:1887–1894Google Scholar
  4. CityGML (2014) Accessed 4.24.14
  5. Dell’Acqua F, Gamba P (2012) Remote sensing and earthquake damage assessment: experiences, limits, and perspectives. Proc IEEE 100:2876–2890. doi: 10.1109/JPROC.2012.2196404 CrossRefGoogle Scholar
  6. Dell’Acqua F, Gamba P, Polli D (2010) Mapping earthquake damage in VHR radar images of human settlements: preliminary results on the 6th April 2009, Italy case. In: Geoscience and remote sensing symposium (IGARSS) 2010 IEEE International, pp 1347–1350. doi: 10.1109/IGARSS.2010.5653973
  7. Dell’Acqua F, Lisini G, Gamba P (2009) Experiences in optical and SAR imagery analysis for damage assessment in the Wuhan, may 2008 earthquake. In: IEEE international geoscience and remote sensing symposium 2009, IGARSS 2009, pp IV–37, IV–40. doi: 10.1109/IGARSS.2009.5417603
  8. Dong L, Shan J (2013) A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS J Photogramm Remote Sens 84:85–99. doi: 10.1016/j.isprsjprs.2013.06.011 CrossRefGoogle Scholar
  9. Feng F, Li Y, Leoreanu-Fotea V (2010) Application of level soft sets in decision making based on interval-valued fuzzy soft sets. Comput Math Appl 60:1756–1767. doi: 10.1016/j.camwa.2010.07.006 CrossRefGoogle Scholar
  10. Gröger G, Kolbe T, Czerwinski A, Nagel C (2008) Open GIS city geography markup language (CityGML) encoding standard (OGC 08-007r1)Google Scholar
  11. Gröger G, Plümer L (2012) Provably correct and complete transaction rules for updating 3D city models. GeoInformatica 16:131–164. doi: 10.1007/s10707-011-0127-6 CrossRefGoogle Scholar
  12. Kagan YY (1997) Are earthquakes predictable? Geophys J Int 131:505–525. doi: 10.1111/j.1365-246X.1997.tb06595.x CrossRefGoogle Scholar
  13. Kolbe TH (2009) Representing and exchanging 3D city models with city GML. In: Lee J, Zlatanova S (eds) 3D Geo-information sciences. Lecture notes in geoinformation and cartography. Springer, Berlin Heidelberg, pp 15–31Google Scholar
  14. Kolbe TH, Gröger G, Plümer L (2005) CityGML: interoperable access to 3D city models. In: van Oosterom PDP, Zlatanova DS, Fendel EM (eds) Geo-information for disaster management. Springer, Berlin, Heidelberg, pp 883–899CrossRefGoogle Scholar
  15. Kubal C, Haase D, Meyer V, Scheuer S (2009) Integrated urban flood risk assessment—adapting a multicriteria approach to a city. Nat Hazards Earth Syst Sci 9:1881–1895. doi: 10.5194/nhess-9-1881-2009 CrossRefGoogle Scholar
  16. Lu D, Mausel P, Brondízio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25:2365–2401. doi: 10.1080/0143116031000139863 CrossRefGoogle Scholar
  17. Lu W, Doihara T, Matsumoto Y (1998) Detection of building changes by integration of aerial imageries and digital maps. Int Arch Photogram Remote Sens 32:244–347Google Scholar
  18. Maji PK, Biswas R, Roy AR (2001) Fuzzy soft sets. Fuzzy Math 9:589–602Google Scholar
  19. Maji PK, Roy AR, Biswas R (2002) An application of soft sets in a decision making problem. Comput Math Appl 44:1077–1083. doi: 10.1016/S0898-1221(02)00216-X CrossRefGoogle Scholar
  20. Martin R, Bernhard H (2010) Change detection of building footprints from airborne laser scanning acquired in short time intervals. Presented at the ISPRS commission VII mid-term symposium 100 Years ISPRS—advancing remote sensing science, Vienna, Austria, pp 475–480Google Scholar
  21. Matsuoka M, Yamazaki F (2004a) Use of satellite SAR intensity imagery for detecting building areas damaged due to earthquakes. Earthq Spectra 20:975–994. doi: 10.1193/1.1774182 CrossRefGoogle Scholar
  22. Matsuoka M, Yamazaki F (2004) Building damage detection using satellite SAR intensity images for the 2003 Algeria and Iran earthquakes. In:IEEE international geoscience and remote sensing symposium 2004, IGARSS ’04, pp. 1099–1102. doi: 10.1109/IGARSS.2009.5417603
  23. Molodtsov D (1999) Soft set theory—first results. Comput Math Appl 37:19–31. doi: 10.1016/S0898-1221(99)00056-5 CrossRefGoogle Scholar
  24. Murakami H, Nakagawa K, Hasegawa H, Shibata T, Iwanami E (1999) Change detection of buildings using an airborne laser scanner. ISPRS J Photogramm Remote Sens 54:148–152. doi: 10.1016/S0924-2716(99)00006-4 CrossRefGoogle Scholar
  25. Park S-E, Yamaguchi Y, Kim D (2013) Polarimetric SAR remote sensing of the 2011 Tohoku earthquake using ALOS/PALSAR. Remote Sens Environ 132:212–220. doi: 10.1016/j.rse.2013.01.018 CrossRefGoogle Scholar
  26. Suga Y, Takeuchi S, Oguro Y, Chen AJ, Ogawa M, Konishi T, Yonezawa C (2001) Application of ERS-2/SAR data for the 1999 Taiwan earthquake. Adv Space Res 28:155–163. doi: 10.1016/S0273-1177(01)00334-9 CrossRefGoogle Scholar
  27. Tong X, Lin X, Feng T, Xie H, Liu S, Hong Z, Chen P (2013) Use of shadows for detection of earthquake-induced collapsed buildings in high-resolution satellite imagery. ISPRS J Photogramm Remote Sens 79:53–67. doi: 10.1016/j.isprsjprs.2013.01.012 CrossRefGoogle Scholar
  28. Trianni G, Gamba P (2009) Fast damage mapping in case of earthquakes using multitemporal SAR data. J Real-Time Image Process 4:195–203. doi: 10.1007/s11554-008-0108-7 CrossRefGoogle Scholar
  29. Vogtle T, Steinle E (2004) Detection and recognition of changes in building geometry derived from multitemporal laserscanning data. Presented at the International Archives of Photogrammetry, Remote Sens Spat Inf Sci, Istanbul, Turkey, pp 428–433Google Scholar
  30. Wang T-L, Jin Y-Q (2012) Postearthquake building damage assessment using multi-mutual information from pre-event optical image and postevent SAR image. IEEE Geosci Remote Sens Lett 9:452–456. doi: 10.1109/LGRS.2011.2170657 CrossRefGoogle Scholar
  31. Yang X, Lin TY, Yang J, Li Y, Yu D (2009) Combination of interval-valued fuzzy set and soft set. Comput Math Appl 58:521–527. doi: 10.1016/j.camwa.2009.04.019 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Iftikhar Ali
    • 1
    Email author
  • Aftab Ahmed Khan
    • 2
  • Salman Qureshi
    • 3
  • Mudassar Umar
    • 4
  • Dagmar Haase
    • 3
  • Ihab Hijazi
    • 5
  1. 1.Department of GeographyUniversity College CorkCorkIreland
  2. 2.Technical University MunichMunichGermany
  3. 3.Department of GeographyHumboldt University of BerlinUnter den LindenGermany
  4. 4.Department of Remote Sensing and GeoinformaticsInstitute of Space TechnologyKarachiPakistan
  5. 5.An-Najah UniversityNablusPalestine

Personalised recommendations