Characterization of elements at risk in the multirisk coastal context and at different spatial scales: Multi-database integration (normandy, France).

Graff, Kevin; Lissak, Candide; Thiéry, Yannick; Maquaire, Olivier; Costa, Stéphane; Medjkane, Mohand

doi:10.1016/j.apgeog.2019.102076

Cited by 7 publications

(2 citation statements)

References 63 publications

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“…In exploring planning strategies for Elements at Risk (EaRs) associated with coastal community resiliency, Graff et al ( 2019 ) identified four operationally defined spatial scales: (i) small-scale (1:100,000–1:250,000), used for regional, strategic planning; (ii) medium-scale (1:25,000–1:50,000), used for identification of critical facilities; (iii) large-scale (1:10,000–1:25,000), used for characterization of infrastructures; and (iv) local-scale analysis (1:2000–1:10,000), which provides more detailed information about the structural components of infrastructure. For design of individual WSUD features within the NbS framework, we would add an additional scale, on the order of 1:100–1:500.…”

Section: Resultsmentioning

confidence: 99%

Nature-based solutions to enhance urban flood resiliency: case study of a Thailand Smart District

Irvine

Suwanarit

Likitswat

et al. 2023

Sustain. Water Resour. Manag.

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A Research through Designing approach was used to explore nature-based solutions (NbS) for flood management at the fluvial (regional) and pluvial (local) scales as part of a Smart District visioning study in a peri-urban area north of Bangkok, Thailand. The NbS visions were informed by community surveys (total n = 770) as well as in-depth, semi-structured interviews with community leaders and key stakeholders representing private sector business. Both fluvial and pluvial flooding commonly occur in the study area and the cost of damage incurred by individuals generally exceeds aid remuneration. The surveys revealed that flood insurance was not widely used as a form of resiliency to flood conditions. Furthermore, survey participants generally considered common space and green space unsatisfactory and inadequate to meet community needs. In light of these survey responses, example NbS visions were developed to address community concerns and promote well-being, while concurrently providing resiliency and improved ecosystem services through connectivity of blue and greenscapes. This case study provides a novel linkage between the concepts of NbS, Research through Designing, and Smart City/District, in exploring sustainable and resilient approaches to flood management in the context of tropical, Global South development and also provides a first step towards developing an NbS typology.

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Section: Resultsmentioning

confidence: 99%

Nature-based solutions to enhance urban flood resiliency: case study of a Thailand Smart District

Irvine

Suwanarit

Likitswat

et al. 2023

Sustain. Water Resour. Manag.

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“…Properties such as building occupancy types, construction types or the number of floors can sometimes be obtained from vertical satellite images (Sarabandi and Kiremidjian 2008), from aspects such as the shadow from buildings, the characteristics of rooftops, and the spatial relationships with other buildings. However, there are many building features that cannot be deduced from vertical images alone, and other data sources are required such as census data, official building databases, cadastral databases, field surveys or volunteered geographic information (VGI) (Graff et al 2019) to provide relevant building information. Therefore, an approach to obtaining such relevant building information that describes the characteristics of the buildings via the use of open source data must be established.…”

Section: Building Characterisationmentioning

confidence: 99%

Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence

et al. 2022

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The mapping and characterisation of building footprints is a challenging task due to inaccessibility and incompleteness of the required data, thus hindering the estimation of loss caused by natural and anthropogenic hazards. Major advancements have been made in the collaborative mapping of buildings with platforms like OpenStreetMap, however, many parts of the world still lack this information or the information is outdated. We created a semi-automated workflow for the development of elements-at-risk (EaR) databases of buildings by detecting building footprints using deep learning and characterising the footprints with building occupancy information using building morphological metrics and open-source auxiliary data. The deep learning model was used to detect building EaR footprints in a city in Kerala (India) with an F1 score of over 76%. The footprints were classified into 13 building occupancy types along with information such as average number of floors, total floor space area, building density, and percentage of built-up area. We analysed the transferability of the approach to a different city in Kerala and obtained an almost similar F1 score of 74%. We also examined the exposure of the buildings and the associated occupancies to floods using the 2018 flood susceptibility map of the respective cities. We notice certain shortcomings in our research particularly, the need for a local expert and good quality auxiliary data to obtain reasonable building occupancy information, however, our research contributes to developing a rapid method for generating a building EaR database in data-scarce regions with attributes of occupancy types, thus supporting regional risk assessment, disaster risk mitigation, risk reduction initiatives, and policy developments.

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A scoping review on the multiplicity of scale in spatial analysis

et al. 2022

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Scale is a central concept in the geographical sciences and is an intrinsic property of many spatial systems. It also serves as an essential thread in the fabric of many other physical and social sciences, which has contributed to the use of different terminology for similar manifestations of what we refer to as ‘scale’, leading to a surprising amount of diversity around this fundamental concept and its various ‘multiscale’ extensions. To address this, we review common abstractions about spatial scale and how they are employed in quantitative research. We also explore areas where the conceptualizations of multiple spatial scales can be differentiated. This is achieved by first bridging terminology and concepts, and then conducting a scoping review of the topic. A typology for spatial scale is discussed that can be used to categorize its multifarious meanings and measures. This typology is then used to distinguish what we term ‘process scale,’ from other types of spatial scale and to highlight current trends in uncovering aspects of process scale. We end with suggestions on how to further build knowledge regarding spatial processes through the lens of spatial scale.

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Characterization of elements at risk in the multirisk coastal context and at different spatial scales: Multi-database integration (normandy, France).

Cited by 7 publications

References 63 publications

Nature-based solutions to enhance urban flood resiliency: case study of a Thailand Smart District

Nature-based solutions to enhance urban flood resiliency: case study of a Thailand Smart District

Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence

A scoping review on the multiplicity of scale in spatial analysis

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