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

Geiß, Christian and Schauß, Anne and Riedlinger, Torsten and Dech, Stefan and Zelaya, Cecilia and Guzman, Nicolas and Hube, Mathias and Jokar Arsanjani, Jamal and Taubenböck, Hannes (2017) Joint use of remote sensing data and volunteered geographic information for exposure estimation: evidence from Valparaíso, Chile. Natural Hazards, 86, pp. 81-105. Springer. DOI: DOI 10.1007/s11069-016-2663-8 ISSN 0921-030X

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Official URL: http://link.springer.com/article/10.1007/s11069-016-2663-8

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information is costly in terms of both time and economic resources and therefore often incomplete, aggregated, or outdated.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 Valparaı´so in Chile. Thereby, we demonstrate the relevance of the approaches by estimating number of affected buildings and population referring to a historical tsunami event.

Item URL in elib:https://elib.dlr.de/111043/
Document Type:Article
Title:Joint use of remote sensing data and volunteered geographic information for exposure estimation: evidence from Valparaíso, Chile
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Geiß, Christianchristian.geiss (at) dlr.deUNSPECIFIED
Schauß, Anneanne.schauss (at) dlr.deUNSPECIFIED
Riedlinger, Torstentorsten.riedlinger (at) dlr.deUNSPECIFIED
Dech, Stefanstefan.dech (at) dlr.deUNSPECIFIED
Zelaya, CeciliaChilean Navy Hydrographic and Oceanographic Service (SHOA)UNSPECIFIED
Guzman, NicolasChilean Navy Hydrographic and Oceanographic Service (SHOA)UNSPECIFIED
Hube, MathiasPontificia Universidad Cato´lica de Chile and National Research Center for Integrated NaturalUNSPECIFIED
Jokar Arsanjani, JamalHeidelberg University, GIScience Research GroupUNSPECIFIED
Taubenböck, Hanneshannes.taubenboeck (at) dlr.deUNSPECIFIED
Date:March 2017
Journal or Publication Title:Natural Hazards
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:86
DOI :DOI 10.1007/s11069-016-2663-8
Page Range:pp. 81-105
Publisher:Springer
ISSN:0921-030X
Status:Published
Keywords:Exposure, Risk, Vulnerability, Remote sensing, Volunteered geographic Information, Land-use–land-cover classification, Object-based image analysis, Rotation forest, Population disaggregation, Tsunami
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Zivile Kriseninformation und Georisiken (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
German Remote Sensing Data Center > Leitungsbereich DFD
Deposited By: Geiß, Christian
Deposited On:14 Feb 2017 08:42
Last Modified:06 Sep 2019 15:21

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