The inevitability of more frequent and more extensive floods, phenomena that display one aspect of the inherent variability of Nature, has been increasingly accepted by managers and policymakers. Climate changes due to global warming add considerable complexity to dealing with flooding, since increases in average temperature alter the patterns and intensity of precipitation. Thus, adverse events arising from natural hazards have tended to become even more frequent and their impacts more severe. Consequently, urban centers face a huge challenge and statistics already show the urgent need for all levels of government to review the adequacy of their plans for anticipating and combating extreme events. Therefore, this paper undertakes a systematic review of the literature, using well-founded and established search strategies, in order to determine the state-of-the-art as presented in 52 peer-reviewed articles strategically selected from two main sources of scientific data. Cross-matching important research metrics from these papers has enabled two axes of discussion to be detailed in this article: a bibliometric analysis and a content analysis. Among many findings, those that stand out are the lack of a formal methodology for climate modeling and the use and sharing of GIS tools, these being key-factors for developing new ways of visualizing risk. Finally, some guidelines for decision-making are put forward in order to share insights and to provide DMs, scholars, experts, stakeholders, and other professionals with an overall background of managerial aspects when engaging on applying flood risk management in urban areas.

pdf/multidimensional-flood-risk-management-under-climate-changes-tq4ijgjump.pdf

Multidimensional flood risk management under climate changes: Bibliometric analysis, trends and strategic guidelines for decision-making in urban dynamics

This article presents a review of the use of unmanned aerial vehicles (UAVs) in the context of geohazards. The pluri-disciplinary role of UAVs is outlined in numerous studies associated with mass earth movements, volcanology, flooding events and earthquakes. Scientific advances and innovations of several research teams around the world are presented from pre-events investigations to crisis management. More particularly, we emphasize the actual status of technology, methodologies and different applications that have emerged with the use of UAVs for each domain. It is shown that the deployment of UAVs in the geohazards context has experienced a tremendous increase during the last 10 years, with the development of more and more miniaturized, flexible and reliable systems. The use of such technology (UAV platform, instrumentation, methodologies) is different for each domain, depending on the spatial extent and the time scale of the observed phenomenon, but also on the practical constraints associated with the civil aviation agencies regulations (outside or within urban areas, before or during a crisis…). This paper also highlights the use of recent methodologies associated with semi-automatic/automatic segmentation or deep learning for the processing of important amounts of data provided by UAVs. Finally, although still sparse, the joint use of UAVs and satellite data is progressing and remains a challenge for future studies in the context of geohazards.

https://link.springer.com/content/pdf/10.1007/s10712-020-09611-7.pdf

Geoscientists in the Sky: Unmanned Aerial Vehicles Responding to Geohazards

Big data and geographic information systems (GIS) are two technologies that have increasingly influenced many areas in the last 10 years and will continue to improve and help solve serious global problems, such as consequences of climate change or global pandemics. A wide spectrum of GIS applications interacts with the continuous growth of geospatial big data sources to drive precise and informed decisions. Geospatial big data integration is designed to accomplish the compatibility of distinct geospatial datasets regardless of their spatial coverage. The large number of geospatial big data sources demand effective data integration for storing and handling such datasets, which will be used for geospatial data analysis and visualization. For instance, risk management datasets related to healthcare and the environment are heterogeneous and disparate. Obtaining a unified view of such geospatial big datasets is complicated and challenging, especially if we consider problems related to healthcare pandemics and environmental disasters. Hence, before we can attempt to predict and mitigate processes occurring in these domains, we must realize that geospatial big data integration is crucial in consolidating datasets. We explore and discuss issues involved in integrating geospatial big datasets in this study. We then classify big data integration processes into three categories, namely, data warehousing, data transformation and integration methods. Furthermore, several research challenges focused on geospatial big data, big earth data, data warehousing, data transformation and linked data are presented. Lastly, open research issues and emerging trends that require in-depth investigations in the near future are highlighted in this study.

/pdf/review-on-integrating-geospatial-big-datasets-and-open-2auodytt3j.pdf

Review on Integrating Geospatial Big Datasets and Open Research Issues

Abstract 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. 

/pdf/mapping-and-characterising-buildings-for-flood-exposure-1o9vbifl.pdf

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

Although the increase in the frequency and intensity of disasters assigns a key role to disaster risk management in current debate on sustainable development, the efforts of national and local authorities to develop risk-informed planning strategies and increase disaster preparedness are still limited. In multi-hazard urban environments, the main criticality to support risk-informed planning strategies is the persisting lack of effective knowledge bases focused on the vulnerability of exposed assets to different hazards. Hence, this contribution, according to the first priority of the Sendai Framework for Disaster Risk Reduction—understanding disaster risk—and by tidying up methods and indicators developed in both EU research projects and scientific studies devoted to multi-risk and vulnerability assessment, aims at better using available knowledge to guide risk-informed spatial planning. In detail, an indicator-based method to carry out a comprehensive exposure and vulnerability analysis has been outlined and tested on a case study area, the multi-hazard urban area of Campi Flegrei, located in the western part of the metropolitan city of Naples in the South of Italy. The proposed method may contribute to the building up of an effective risk knowledge base, enabling planners to easily access information on exposure and vulnerabilities to different hazards, and to differently combine them into output maps capable of supporting risk- informed planning strategies.

https://www.mdpi.com/2071-1050/13/16/9055/pdf

A Comprehensive Assessment of Exposure and Vulnerabilities in Multi-Hazard Urban Environments: A Key Tool for Risk-Informed Planning Strategies

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

Coastal environment with high interaction between nature and societies is subject to multi-hazard interaction such as landslides, flood or cliff retreat. These territories are characterized by numerous elements at risk located in valley bottoms, front sea or at the outlets of small dry watershed. The aim is to quantify the potential consequences of EaR by integrating multiple hazards exposure at various scale analyses. To quantify the element at risk, three steps have been required. First, an initial rank has been attributed to each class of element at risk at three different scales analysis. Second, the potential consequences are weighted according to environmental dimension. Third, the consequences are combined with a linear combination of criteria in GIS environment. At medium-scale analysis, element at risk highlighted is built-up areas, national road, railway, lifeline and urban centers. At large-scale analysis, consequences concern any kind of house, apartment and complex located on multiple exposure areas. At local scale, consequences concern buildings located on multiple exposure areas with one floor in mixed materials and built after 1980. Thus, this method proposes an approach with multiple scales analysis and by integrating multiple exposure areas to quantify potential consequences. With the environmental dimension in element at risk analysis, it is an intermediate step to traditional risk analysis and, more specifically multirisk analysis without considering in this case the spatial and temporal dimension of hazards.

Analysis and quantification of potential consequences in multirisk coastal context at different spatial scales (Normandy, France)

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

Analysis and quantification of potential consequences in multirisk coastal context at different spatial scales (Normandy, France)