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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

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3D Geoinformation Science

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))

Abstract

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.

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Acknowledgments

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.

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Authors and Affiliations

  1. Department of Geography, University College Cork, Cork, Ireland

    Iftikhar Ali

  2. Technical University Munich, Munich, Germany

    Aftab Ahmed Khan

  3. Department of Geography, Humboldt University of Berlin, Unter den Linden, Berlin, Germany

    Salman Qureshi & Dagmar Haase

  4. Department of Remote Sensing and Geoinformatics, Institute of Space Technology, 75270, Karachi, Pakistan

    Mudassar Umar

  5. An-Najah University, Nablus, Palestine

    Ihab Hijazi

Authors
  1. Iftikhar Ali
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  2. Aftab Ahmed Khan
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  3. Salman Qureshi
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  4. Mudassar Umar
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  5. Dagmar Haase
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  6. Ihab Hijazi
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Corresponding author

Correspondence to Iftikhar Ali .

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Editors and Affiliations

  1. Geodetic Institute, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Martin Breunig

  2. School of Engineering, American University in Dubai, Dubai, Utd.Arab.Emir.

    Mulhim Al-Doori

  3. Geodetic Institute, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Edgar Butwilowski

  4. Geodetic Institute, Karlsruhe Institute of Technology, Karlsruhe, Baden-WĂĽrttemberg, Germany

    Paul V. Kuper

  5. Institute for Applied Computer Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Joachim Benner

  6. Institute for Applied Computer Science, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Karl Heinz Haefele

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Ali, I., Khan, A.A., Qureshi, S., Umar, M., Haase, D., Hijazi, I. (2015). 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. In: Breunig, M., Al-Doori, M., Butwilowski, E., Kuper, P., Benner, J., Haefele, K. (eds) 3D Geoinformation Science. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-12181-9_6

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