Cited by 387Remote Sensing and Land UseDisaster Management and ResilienceSatellite Image Processing and Photogrammetry

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Satellite Image Analysis for Disaster and Crisis-Management Support

Voigt, Stefan, Kemper, Thomas, Riedlinger, Torsten, Kiefl, Ralph, Scholte, Klaas, Mehl, Harald·2007·IEEE Transactions on Geoscience and Remote Sensing

DOI 10.1109/tgrs.2007.895830

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Abstract

This paper describes how multisource satellite data and efficient image analysis may successfully be used to conduct rapid-mapping tasks in the domain of disaster and crisis-management support. The German Aerospace Center (DLR) has set up a dedicated crosscutting service, which is the so-called "Center for satellite-based Crisis Information" (ZKI), to facilitate the use of its Earth-observation capacities in the service of national and international response to major disaster situations, humanitarian relief efforts, and civil security issues. This paper describes successful rapid satellite mapping campaigns supporting disaster relief and demonstrates how this technology can be used for civilian crisis-management purposes. During the last years, various international coordination bodies were established, improving the disaster-response-related cooperation within the Earth-observation community worldwide. DLR/ZKI operates in this context, closely networking with public authorities (civil security), nongovernmental organizations (humanitarian relief organizations), satellite operators, and other space agencies. This paper reflects on several of these international activities, such as the International Charter Space and Major Disasters, describes mapping procedures, and reports on rapid-mapping experiences gained during various disaster-response applications. The example cases presented cover rapid impact assessment after the Indian Ocean Tsunami, forest fires mapping for Portugal, earthquake-damage assessment for Pakistan, and landslide extent mapping for the Philippines

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2000, Vector Map Level 0 (Digital Chart of the World)
0, GEOnet Names Server (GNS)
matsuoka, 2001, image processing of building damage detection due to disasters using sar intensity images, Proc 24th Conf Remote Sensing Soc Jpn, 269
0, Terminology of disaster risk reduction
ehrlich, 2006, Standardized damage assessments and reportingA methodology that combines field data and satellite data analysis for improved damage reporting
2000, Space and Major Disasters
brakenridge, 2003, satellite gaging reaches: a strategy for modis-based river monitoring, Proc 9th Int Symp Remote Sens Int Soc Opt Eng (SPIE), 479
2002, The use of earth observation satellites for hazard support Assessments & scenarios Final report of the CEOS Disaster Management Support Group

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Authors: Voigt, S., Kemper, T., Riedlinger, T., Kiefl, R., Scholte, K., & Mehl, H.
Affiliations: Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
ORCID: Voigt, Stefan
Kemper, Thomas
Riedlinger, Torsten
Kiefl, Ralph
Venue: IEEE Transactions on Geoscience and Remote Sensing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2007-06
Volume: 45
Issue: 6
Pages: 1520-1528
ISSN: 0196-2892 (ISSN-L)
DOI: 10.1109/tgrs.2007.895830
OpenAlex: W2170535121
Cited by: 387
References: 26
Citations by year: 2026: 19 · 2025: 25 · 2024: 21 · 2023: 25 · 2022: 25 · 2021: 22 · 2020: 27 · 2019: 29 · 2018: 19 · 2017: 17 · 2016: 19 · 2015: 23 · 2014: 22 · 2013: 14 · 2012: 25
FWCI: 7.78
Type: journal article
Language: en
Open access: closed
Locations: IEEE Transactions on Geoscience and Remote Sensing (publishedVersion)
elib (German Aerospace Center) (acceptedVersion)
Funders: European Commission
Joint Research Centre
Topics: Social Sciences › Geography, Planning and Development › Remote Sensing and Land Use
Social Sciences › Sociology and Political Science › Disaster Management and Resilience
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Satellite Image Processing and Photogrammetry
Keywords: image analysis, International Charter Space and Major Disasters, humanitarian aid, civil security, disaster impact assessment, earthquake damage assessment, landslide mapping, Indian Ocean tsunami, Pakistan earthquake

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Satellite Image Analysis for Disaster and Crisis-Management Support

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