Person:
Aubrecht, Christoph

European Space Agency Representative
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Geospatial analytics, Remote sensing, Earth observation, Spatial analysis, Sustainable development
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European Space Agency Representative
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Last updated: January 31, 2023
Biography
Christoph Aubrecht is affiliated with the European Space Agency (ESA), representing ESA at the World Bank to coordinate collaborative activities. Prior to joining ESA, Chris was leading the spatial analytics efforts under the World Bank’s Central America & Caribbean CDRP initiative. For more than 10 years Chris also worked at the AIT Austrian Institute of Technology, most recently serving as senior advisor on geospatial strategy development and implementation design. Further previous affiliations include senior consultancy positions at the World Bank’s DRM/Urban unit, short-term consultancy at GFDRR and various visiting scientist positions at NOAA’s NGDC, at Columbia University’s CIESIN and the attached NASA SEDAC, as well as at the U. of Southern California. From 2008-2015 Chris served as adjunct lecturer in GI science and remote sensing at U. of Vienna. He is on various scientific journal editorial boards and a regular member on international project advisory committees in the domain of integrated spatial analytics. Chris holds a PhD in integrated GI science and remote sensing from Vienna U. of Technology and a prior Master’s degree in geography and GI science from the U. of Vienna.
Citations 62 Scopus

Publication Search Results

Now showing 1 - 3 of 3
  • Publication
    Developing an Adaptive Global Exposure Model to Support the Generation of Country Disaster Risk Profiles
    (Elsevier, 2015-09-08) Gunasekera, Rashmin; Ishizawa, Oscar; Aubrecht, Christoph; Blankespoor, Brian; Murray, Siobhan; Pomonis, Antonios; Daniell, James
    Corresponding to increased realization of the impacts of natural hazards in recent years and the need for quantification of disaster risk, there has been increasing demand from the public sector for openly available disaster risk profiles. Probabilistic disaster risk profiles provide risk assessments and estimates of potential damage to property caused by severe natural hazards. These profiles outline a holistic view of financial risk due to natural hazards, assisting governments in long-term planning and preparedness. A Country Disaster Risk Profile (CDRP) presents a probabilistic estimate of risk aggregated at the national level. A critical component of a CDRP is the development of consistent and robust exposure model to complement existing hazard and vulnerability models. Exposure is an integral part of any risk assessment model, capturing the attributes of all exposed elements grouped by classes of vulnerability to different hazards, and analyzed in terms of value, location and relative importance (e.g. critical facilities and infrastructure). Using freely available (or available at minimum cost) datasets, we present a methodology for an exposure model to produce three independent geo-referenced databases to be used in national level disaster risk profiling for the public sector. These databases represent aggregated economic value at risk at 30 arc-second spatial resolution (approximately 1 × 1-km grid at the equator) using a top-down (or downscaling) approach. To produce these databases, the models used are: 1) a building inventory stock model which captures important attributes such as geographical location, urban/rural classification, type of occupancy (e.g. residential and non-residential), building typology (e.g. wood, concrete, masonry, etc.) and economic (replacement) value; 2) a non-building infrastructure density and value model that also corresponds to the fiscal infrastructure portion of the Gross Capital Stock (GCS) of a country; and 3) a spatially and sectorially disaggregated Gross Domestic Product (GDP) model that relates to the production (flow) of goods and services of a country. These models can be adapted to produce - independently or cohesively - a composite exposure database. Finally, we provide an example of the model's use in economic loss estimation for the reoccurrence of the 1882 Mw 7.8 Panama earthquake.
  • Publication
    Consistent Yet Adaptive Global Geospatial Identification of Urban–Rural Patterns: The iURBAN Model
    (Elsevier, 2016-12) Aubrecht, Christoph; Ungar, Joachim; Ishizawa, Oscar
    The main motivation of this paper is to shed new light on the problem of spatial identification of urban and rural areas globally, and to provide a compatible disaggregation framework for linking associated country-specific, non-spatial data compilations, such as building type inventories. Existing homogeneously set-up global urban extent models commonly ignore local-level specifics. While global consistency and regional comparability of urban characteristics are much strived-for goals in the global development and remote sensing communities, non-conformity at the national level often renders such models inapplicable for effective decision-making. Furthermore, the focus on identifying ‘urban’ leads to an ill-defined ‘rural’, which is simply defined by contrast as ‘everything else’; a questionable definition when referring to strongly spatially localized residential patterns. In this paper we introduce the novel iURBAN geospatial modeling approach, identifying Urban–Rural patterns in Built-up-Adjusted and Nationally-adaptive manner. The model operates at global scale, but at the same time conforms to country specifics. In this model, high-resolution, satellite-derived, built-up data is used to consistently detect global human settlements at unprecedented spatial detail. In combination with global gridded population data, and with reference to national level statistical information on urban population ratios globally compiled in the annually-released UN World Urbanization Prospects, iURBAN identifies matching urban extents. Additionally, a novel reallocation algorithm is introduced which addresses the poor representation of rural areas that is inherent in existing global population grids. Associating all of the population with inhabitable, built-up area and conforming to national urban–rural ratios, iURBAN sets a new standard by enabling careful consideration of both urban and rural as opposed to traditional urban-biased approaches.
  • Publication
    Evaluating Multi-Sensor Nighttime Earth Observation Data for Identification of Mixed vs. Residential Use in Urban Areas
    (MDPI, 2016-02-04) León Torres, José Antonio; Aubrecht, Christoph
    This paper introduces a novel top-down approach to geospatially identify and distinguish areas of mixed use from predominantly residential areas within urban agglomerations. Under the framework of the World Bank’s Central American Country Disaster Risk Profiles (CDRP) initiative, a disaggregated property stock exposure model has been developed as one of the key elements for disaster risk and loss estimation. Global spatial datasets are therefore used consistently to ensure wide-scale applicability and transferability. Residential and mixed use areas need to be identified in order to spatially link accordingly compiled property stock information. In the presented study, multi-sensor nighttime Earth Observation data and derivative products are evaluated as proxies to identify areas of peak human activity. Intense artificial night lighting in that context is associated with a high likelihood of commercial and/or industrial presence. Areas of low light intensity, in turn, can be considered more likely residential. Iterative intensity thresholding is tested for Cuenca City, Ecuador, in order to best match a given reference situation based on cadastral land use data. The results and findings are considered highly relevant for the CDRP initiative, but more generally underline the relevance of remote sensing data for top-down modeling approaches at a wide spatial scale.