Estimation of the regional stock of residential buildings as a basis for a comparative risk assessment in Germany

Kleist, L.; Thieken, Annegret H.; Köhler, Petra; Müller, Matthias; Seifert, Isabel; Borst, Dietmar; Werner, Ulrich

doi:10.5194/nhess-6-541-2006

Cited by 75 publications

(63 citation statements)

References 9 publications

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“…To estimate property values, the total asset value of all residential buildings in Cologne was taken from Kleist et al (2006). Since only the total sum was provided, the assets were disaggregated on the basis of the CORINE land cover data 2000 by means of a dasymetric mapping approach that is outlined in Thieken et al (2006).…”

Section: Damage Estimationmentioning

confidence: 99%

Flood risk curves and uncertainty bounds

2009

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Although flood risk assessments are frequently associated with significant uncertainty, formal uncertainty analyses are the exception rather than the rule. We propose to separate two fundamentally different types of uncertainty in flood risk analyses: aleatory and epistemic uncertainty. Aleatory uncertainty refers to quantities that are inherently variable in time, space or populations of individuals or objects. Epistemic uncertainty results from incomplete knowledge and is related to our inability to understand, measure and describe the system under investigation. The separation between aleatory and epistemic uncertainty is exemplified for the flood risk analysis of the City of Cologne, Germany. This flood risk assessment consists of three modules, (1) flood frequency analysis, (2) inundation estimation, and (3) damage estimation. By the concept of parallel models, the epistemic uncertainty of each module is quantified. The epistemic uncertainty associated with the risk estimate is reduced by introducing additional information into the risk analysis. Finally, the contribution of different modules to the total uncertainty is quantified. The flood risk analysis results in a flood risk curve, representing aleatory uncertainty, and in associated uncertainty bounds, representing epistemic uncertainty. In this way, the separation reveals the uncertainty (epistemic) that can be reduced by more knowledge and the uncertainty (aleatory) that is not reducible.

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Section: Damage Estimationmentioning

confidence: 99%

Flood risk curves and uncertainty bounds

2009

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“…Dutta et al, 2003). If these functions are used to estimate the loss due to a given flood scenario property values have to be predetermined (Kleist et al, 2004(Kleist et al, , 2006. However, using these functions, one has to be aware that the damage estimation is generally associated with large uncertainties as recent studies asserted .…”

Section: Basis and Objectivesmentioning

confidence: 99%

Flood-risk mapping: contributions towards an enhanced assessment of extreme events and associated risks

Büchele

Kreibich

Kron

et al. 2006

Nat. Hazards Earth Syst. Sci.

265

185

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Abstract. Currently, a shift from classical flood protection as engineering task towards integrated flood risk management concepts can be observed. In this context, a more consequent consideration of extreme events which exceed the design event of flood protection structures and failure scenarios such as dike breaches have to be investigated. Therefore, this study aims to enhance existing methods for hazard and risk assessment for extreme events and is divided into three parts. In the first part, a regionalization approach for flood peak discharges was further developed and substantiated, especially regarding recurrence intervals of 200 to 10 000 years and a large number of small ungauged catchments. Model comparisons show that more confidence in such flood estimates for ungauged areas and very long recurrence intervals may be given as implied by statistical analysis alone. The hydraulic simulation in the second part is oriented towards hazard mapping and risk analyses covering the whole spectrum of relevant flood events. As the hydrodynamic simulation is directly coupled with a GIS, the results can be easily processed as local inundation depths for spatial risk analyses. For this, a new GIS-based software tool was developed, being presented in the third part, which enables estimations of the direct flood damage to single buildings or areas based on different established stage-damage functions. Furthermore, a new multifactorial approach for damage estimation is presented, aiming at the improvement of damage estimation on local scale by considering factors like building quality, contamination and precautionary measures. The methods and results from this study form the base for comprehensive risk analyses and flood management strategies.

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“…The number of floors in a building is a relevant piece of information when assessing exposure of urban areas to natural threats [39], but large-scale mapping of such a parameter on every individual building requires a large effort. Satellite images, a typical choice for large-scale mapping, may not offer the desired information content due to e.g., a nadiral view suppressing height information.…”

Section: Discussionmentioning

confidence: 99%

Extensive Exposure Mapping in Urban Areas through Deep Analysis of Street-Level Pictures for Floor Count Determination

Iannelli¹,

Dell’Acqua

2017

Urban Science

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Abstract:In order for a risk assessment to deliver sensible results, exposure in the concerned area must be known or at least estimated in a reliable manner. Exposure estimation, though, may be tricky, especially in urban areas, where large-scale surveying is generally expensive and impractical; yet, it is in urban areas that most assets are at stake when a disaster strikes. Authoritative sources such as cadastral data and business records may not be readily accessible to private stakeholders such as insurance companies; airborne and especially satellite-based Earth-Observation data obviously cannot retrieve all relevant pieces of information. Recently, a growing interest is recorded in the exploitation of street-level pictures, procured either through crowdsourcing or through specialized services like Google Street View. Pictures of building facades convey a great amount of information, but their interpretation is complex. Recently, however, smarter image analysis methods based on deep learning started appearing in literature, made possible by the increasing availability of computational power. In this paper, we leverage such methods to design a system for large-scale, systematic scanning of street-level pictures intended to map floor numbers in urban buildings. Although quite simple, this piece of information is a relevant exposure proxy in risk assessment. In the proposed system, a series of georeferenced images are automatically retrieved from the repository where they sit. A tailored deep learning net is first trained on sample images tagged through visual interpretation, and then systematically applied to the entire retrieved dataset. A specific algorithm allows attaching "number of floors" tags to the correct building in a dedicated GIS (Geographic Information System) layer, which is finally output by the system as an "exposure proxy" layer.

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Estimation of the regional stock of residential buildings as a basis for a comparative risk assessment in Germany

Cited by 75 publications

References 9 publications

Flood risk curves and uncertainty bounds

Flood risk curves and uncertainty bounds

Flood-risk mapping: contributions towards an enhanced assessment of extreme events and associated risks

Extensive Exposure Mapping in Urban Areas through Deep Analysis of Street-Level Pictures for Floor Count Determination

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