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Joint use of remote sensing data and volunteered geographic information for exposure estimation: evidence from Valparaíso, Chile

Author

Listed:
  • Christian Geiß

    (German Remote Sensing Data Center (DFD))

  • Anne Schauß

    (German Remote Sensing Data Center (DFD)
    Heidelberg University, GIScience Research Group)

  • Torsten Riedlinger

    (German Remote Sensing Data Center (DFD))

  • Stefan Dech

    (German Remote Sensing Data Center (DFD))

  • Cecilia Zelaya

    (Chilean Navy Hydrographic and Oceanographic Service (SHOA))

  • Nicolás Guzmán

    (Chilean Navy Hydrographic and Oceanographic Service (SHOA))

  • Mathías A. Hube

    (Pontificia Universidad Católica de Chile and National Research Center for Integrated Natural Disaster Management CONICYT/FONAP/15110017)

  • Jamal Jokar Arsanjani

    (Heidelberg University, GIScience Research Group)

  • Hannes Taubenböck

    (German Remote Sensing Data Center (DFD))

Abstract

The impact of natural hazards on mankind has increased dramatically over the past decades. Global urbanization processes and increasing spatial concentrations of exposed elements induce natural hazard risk at a uniquely high level. To mitigate affiliated perils requires detailed knowledge about elements at risk. Considering a high spatiotemporal variability of elements at risk, detailed information is costly in terms of both time and economic resources and therefore often incomplete, aggregated, or outdated. To alleviate these restrictions, the availability of very-high-resolution satellite images promotes accurate and detailed analysis of exposure over various spatial scales with large-area coverage. In the past, valuable approaches were proposed; however, the design of information extraction procedures with a high level of automatization remains challenging. In this paper, we uniquely combine remote sensing data and volunteered geographic information from the OpenStreetMap project (OSM) (i.e., freely accessible geospatial information compiled by volunteers) for a highly automated estimation of crucial exposure components (i.e., number of buildings and population) with a high level of spatial detail. To this purpose, we first obtain labeled training segments from the OSM data in conjunction with the satellite imagery. This allows for learning a supervised algorithmic model (i.e., rotation forest) in order to extract relevant thematic classes of land use/land cover (LULC) from the satellite imagery. Extracted information is jointly deployed with information from the OSM data to estimate the number of buildings with regression techniques (i.e., a multi-linear model from ordinary least-square optimization and a nonlinear support vector regression model are considered). Analogously, urban LULC information is used in conjunction with OSM data to spatially disaggregate population information. Experimental results were obtained for the city of Valparaíso in Chile. Thereby, we demonstrate the relevance of the approaches by estimating number of affected buildings and population referring to a historical tsunami event.

Suggested Citation

  • Christian Geiß & Anne Schauß & Torsten Riedlinger & Stefan Dech & Cecilia Zelaya & Nicolás Guzmán & Mathías A. Hube & Jamal Jokar Arsanjani & Hannes Taubenböck, 2017. "Joint use of remote sensing data and volunteered geographic information for exposure estimation: evidence from Valparaíso, Chile," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 86(1), pages 81-105, March.
  • Handle: RePEc:spr:nathaz:v:86:y:2017:i:1:d:10.1007_s11069-016-2663-8
    DOI: 10.1007/s11069-016-2663-8
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    References listed on IDEAS

    as
    1. Christian Geiß & Hannes Taubenböck, 2013. "Remote sensing contributing to assess earthquake risk: from a literature review towards a roadmap," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(1), pages 7-48, August.
    2. D. Ehrlich & T. Kemper & X. Blaes & P. Soille, 2013. "Extracting building stock information from optical satellite imagery for mapping earthquake exposure and its vulnerability," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(1), pages 79-95, August.
    3. Christoph Aubrecht & Dilek Özceylan & Klaus Steinnocher & Sérgio Freire, 2013. "Multi-level geospatial modeling of human exposure patterns and vulnerability indicators," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(1), pages 147-163, August.
    4. Mordechai Haklay, 2010. "How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets," Environment and Planning B, , vol. 37(4), pages 682-703, August.
    5. Daniele Ehrlich & Patrizia Tenerelli, 2013. "Optical satellite imagery for quantifying spatio-temporal dimension of physical exposure in disaster risk assessments," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(3), pages 1271-1289, September.
    6. Susan L. Cutter & Bryan J. Boruff & W. Lynn Shirley, 2003. "Social Vulnerability to Environmental Hazards," Social Science Quarterly, Southwestern Social Science Association, vol. 84(2), pages 242-261, June.
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    1. Kiatkulchai Jitt-Aer & Graham Wall & Dylan Jones & Richard Teeuw, 2022. "Use of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(1), pages 185-211, August.

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