Joint use of remote sensing data and volunteered geographic information for exposure estimation

• Verfasst von: Geiß, Christian [VerfasserIn]
• Verfasst von: Schauß, Anne [VerfasserIn]
• Verfasst von: Jokar Arsanjani, Jamal [VerfasserIn]
• Titel: Joint use of remote sensing data and volunteered geographic information for exposure estimation
• Titelzusatz: evidence from Valparaíso, Chile
• Verf.angabe: Christian Geiß, Anne Schauß, Torsten Riedlinger, Stefan Dech, Cecilia Zelaya, Nicolás Guzmán, Mathías A. Hube, Jamal Jokar Arsanjani, Hannes Taubenböck
• Jahr des Originals: 2016
• Umfang: 25 S.
• Fussnoten: Published online: 08 November 2016 ; Gesehen am 27.04.2018
• Titel Quelle: Enthalten in: Natural hazards
• Jahr Quelle: 2017
• Band/Heft Quelle: 86(2017), 1, S. 81-105
• ISSN Quelle: 1573-0840
• DOI: doi:10.1007/s11069-016-2663-8
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1572442166

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information is costly in terms of both time and economic resources and therefore often incomplete, aggregated, or outdated.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.