Documenting cultural heritage in an INSPIRE-based 3D GIS for risk and vulnerability analysis

Purpose The study, within the Increasing Resilience of Cultural Heritage (ResCult) project, aims to support civil protection to prevent, lessen and mitigate disasters impacts on cultural heritage using a unique standardised-3D geographical information system (GIS), including both heritage and risk and hazard information. Design/methodology/approach A top-down approach, starting from existing standards (an INSPIRE extension integrated with other parts from the standardised and shared structure), was completed with a bottom-up integration according to current requirements for disaster prevention procedures and risk analyses. The results were validated and tested in case studies (differentiated concerning the hazard and type of protected heritage) and refined during user forums. Findings Besides the ensuing reusable database structure, the filling with case studies data underlined the tough challenges and allowed proposing a sample of workflows and possible guidelines. The interfaces are provided to use the obtained knowledge base. Originality/value The increasing number of natural disasters could severely damage the cultural heritage, causing permanent damage to movable and immovable assets and tangible and intangible heritage. The study provides an original tool properly relating the (spatial) information regarding cultural heritage and the risk factors in a unique archive as a standard-based European tool to cope with these frequent losses, preventing risk

A Study of Types of Sensors used in Remote Sensing

Of late, the science of Remote Sensing has been gaining a lot of interest and attention due to its wide variety of applications. Remotely sensed data can be used in various fields such as medicine, agriculture, engineering, weather forecasting, military tactics, disaster management etc. only to name a few. This article presents a study of the two categories of sensors namely optical and microwave which are used for remotely sensing the occurrence of disasters such as earthquakes, floods, landslides, avalanches, tropical cyclones and suspicious movements. The remotely sensed data acquired either through satellites or through ground based- synthetic aperture radar systems could be used to avert or mitigate a disaster or to perform a post-disaster analysis

A Study of Types of Sensors used in Remote Sensing

Of late, the science of Remote Sensing has been gaining a lot of interest and attention due to its wide variety of applications. Remotely sensed data can be used in various fields such as medicine, agriculture, engineering, weather forecasting, military tactics, disaster management etc. only to name a few. This article presents a study of the two categories of sensors namely optical and microwave which are used for remotely sensing the occurrence of disasters such as earthquakes, floods, landslides, avalanches, tropical cyclones and suspicious movements. The remotely sensed data acquired either through satellites or through ground based- synthetic aperture radar systems could be used to avert or mitigate a disaster or to perform a post-disaster analysis

Exposure and vulnerability estimation for modelling flood losses to commercial assets in Europe

open6siThis workwas supported by Climate-KIC through project“SAFERPLACES–Improved assessment of pluvial,fluvial and coastalflood hazards and risks in European cities as a mean to build safer andresilient communities”, Task ID TC2018B_4.7.3-SAFERPL_P430-1AKAVA2 4.7.3. Further funding was received from the European Union'sHorizon 2020 research and innovation programme under grant agree-ment no. 730381. The post-disaster surveys were carried out by a jointventure between the German Research Centre for Geosciences GFZ,the University of Potsdam and the Deutsche Rueckversicherung AG,Duesseldorf. The surveys were further supported by the German Minis-try of Education and Research (BMBF) through projects DFNK01SFR9969/5 and“Hochwasser 2013”(13N13017).Commercial assets comprise buildings, machinery and equipment, which are susceptible to floods. Existing damage models and exposure estimation methods for this sector have limited transferability between flood events and therefore limited potential for pan-European applications. In this study we introduce two methodologies aiming at improving commercial flood damage modelling: (1) disaggregation of economic statistics to obtain detailed building-level estimates of replacement costs of commercial assets; (2) a Bayesian Network (BN) damage model based primarily on post-disaster company surveys carried out in Germany. The BN model is probabilistic and provides probability distributions of estimated losses, and as such quantitative uncertainty information. The BN shows good accuracy of predictions of building losses, though overestimates machinery/equipment loss. To test its suitability for pan-European flood modelling, the BN was applied to three case studies, comprising a coastal flood in France (2010) and fluvial floods in Saxony (2013) and Italy (2014). Overall difference between modelled and reported average loss per company was only 2–19% depending on the case study. Additionally, the BN model achieved better results than six alternative damage models in those case studies (except for one model in the Italian case study). Further, our exposure estimates mostly resulted in better predictions of the damage models compared to previously published pan-European exposure data, which tend to overestimate exposure. All in all, the methods allow easy modelling of commercial flood losses in the whole of Europe, since they are applicable even if only publicly-available datasets are obtainable. The methods achieve a higher accuracy than alternative approaches, and inherently provide confidence intervals, which is particularly valuable for decision making under high uncertainty.embargoed_20220616Paprotny D.; Kreibich H.; Morales-Napoles O.; Castellarin A.; Carisi F.; Schroter K.Paprotny D.; Kreibich H.; Morales-Napoles O.; Castellarin A.; Carisi F.; Schroter K

A critical review for the application of Cutting-edge Digital Visualisation Technologies for Effective Urban Flood Risk Management

Cutting-edge digital visualisation tools (CDVT) are playing an increasingly important role in improving urban flood risk management. However, there is a paucity of comprehensive research examining their role across all stages of urban flood risk management. To address, this study conducts an integrated critical review to identify the application of CDVT and assess their contribution to the prevention, mitigation, preparation, response, and recovery stages of flood risk management. The results show that virtual reality, augmented reality, and digital twin technologies are the primary CDVT used in urban flood visualisation, with virtual reality being the most frequently used. The focus of urban flood visualisation studies has been primarily on preparation and mitigation stages. However, there is a need to investigate the application of these technologies in the entire urban water cycle. Furthermore, there is potential for greater adoption of digital twin, especially in simulating urban flood inundation and flood evacuation routes. Integrating real-time data, data-driven modeling, and CDVT can significantly improve real-time flood forecasting. This benefits stakeholders and the public by enhancing early warning systems, preparedness, and flood resilience, leading to more effective flood risk management and reduced impacts on communities

Building structural characterization using mobile terrestrial point cloud for flood risk anticipation

Compte tenu de la fréquence élevée et de l'impact majeur des inondations, les décideurs, les acteurs des municipalités et le ministère de la sécurité publique ont un besoin urgent de disposer d'outils permettant de prédire ou d'évaluer l'importance des inondations et leur impact sur la population. D'après les statistiques, le premier étage des bâtiments, ainsi que les ouvertures inférieures, sont plus susceptibles de subir des dommages lors d'une inondation. Ainsi, dans le cadre de l'évaluation de l'impact des inondations, il serait nécessaire d'identifier l'emplacement de l'ouverture la plus basse des bâtiments et surtout sa hauteur par rapport au sol. Le système de balayage laser mobile (MLS) monté sur un véhicule s'est avéré être l'une des sources les plus fiables pour caractériser les bâtiments. Il peut produire des millions de points géoréférencés en 3D avec un niveau de détail suffisant, grâce à son point de vue depuis la rue et sa proximité. De plus, l'augmentation du nombre de jeux de données, issues des MLS acquis dans les villes et les environnements ruraux, permet de développer des approches pour caractériser les maisons résidentielles à l'échelle provinciale. Plusieurs défis sont associés à l'extraction d'informations descriptives des façades de bâtiments à l'aide de données MLS. Ainsi, les occlusions devant une façade rendent impossible l'obtention de points 3D sur ces parties de la façade. Aussi, comme les fenêtres sont principalement constituées de verre, qui ne réfléchit pas les signaux laser, les points disponibles pour celles-ci sont généralement limités. De plus, les approches de détection exploitent la répétitivité et les positions symétriques des ouvertures sur la façade. Mais ces caractéristiques sont absentes pour des maisons rurales et résidentielles. Finalement, la variabilité de la densité de points dans les données MLS rend difficile le processus de détection lorsqu'on travaille à l'échelle d'une ville. Par conséquent, l'objectif principal de cette recherche est de concevoir et de développer une approche globale d'extraction efficace des ouvertures présentes sur une façade. La solution proposée se compose de trois phases: l'extraction des façades, la détection des ouvertures et l'identification des occlusions. La première phase utilise une approche de segmentation adaptative par croissance de régions pour extraire la boîte englobante 3D de la façade. La deuxième phase combine la détection de trous avec une technique de maillage pour extraire les boîtes englobantes 2D des ouvertures. La dernière phase, qui vise à discriminer les occlusions des ouvertures, est en cours d'achèvement. Des évaluations qualitatives et quantitatives ont été réalisées à l'aide d'un jeu de données réelles, fourni par Jakarto Cartographie 3D Inc., de la province de Québec, au Canada. Les statistiques ont révélé que l'approche proposée pouvait obtenir de bons taux de performance malgré la complexité du jeu de données, représentatif des données acquises en situation réelle. Les défis concernant l'auto-occlusion de certaines façades et la présence de grandes occlusions environnantes seront à étudier plus en profondeur afin d'obtenir des informations plus précises sur les ouvertures des façades.Given the high frequency and major impact of floods, decision-makers, stakeholders in municipalities and public security ministry are in the urgent need to have tools allowing to predict or assess the significance of flood events and their impact on the population. Based on statistics, the first floor of the buildings, as well as the lower openings, are more likely subject to potential damage during a flood event. Thus, in the context of flood impact assessment, it would be required identifying the location of the buildings' lowest opening and especially its height above the ground. The capacity to characterize building with a relevant level of detail depends on the data sources used for the modeling. Different sources of data have been employed to characterize buildings' façade and openings. Mobile Laser Scanning (MLS) system mounted on a vehicle has proved to be one of the most reliable sources in this domain. It can produce millions of 3D georeferenced points with sufficient level of detail of the building facades and its openings, due to its street-view and close-range distance. Moreover, the increase of MLS providers and acquisitions in towns and rural environments, makes it possible to develop approaches to characterize residential houses at a provincial scale. Although being effective, several challenges are associated with extracting descriptive information of building facades using MLS data. The presence of occlusion in front of a facade makes it impossible to obtain the 3D points of the covered parts of the facade. Given the fact that windows mostly consist of glass and laser signals could not be reflected from the glass, limited points are usually available for windows. While the repetitive pattern and symmetrical positions of the openings on the facade makes it easier for the detection system to extract them, this characteristic is missing on the facade on rural and residential houses. The inconsistency of the point density in MLS data make the detection process even harder when working at city scale. Accordingly, the main objective of this research is to design and develop a comprehensive approach that effectively extracts facade openings. In order to meet the research project objective, the proposed solution consists of three phases including facade extraction, opening detection, and occlusion recognition. The first phase employs an adaptive region growing segmentation approach to extract the 3D bounding box of the facade. The second phase combines a hole-based assumption with an XZ gridding technique to extract 2D bounding boxes of the openings. The last phase which recognizes holes related to the occlusion from the openings is currently being completed. Qualitative and quantitative evaluations were performed using a real-word dataset provided by Jakarto Cartographie 3D inc. of the Quebec Province, Canada. Statistics revealed that the proposed approach could obtain good performance rates despite the complexity of the dataset, representative of the data acquired in real situations. Challenges regarding facade's self-occlusion and the presence of large surrounding occlusions should be further investigated for obtaining more accurate opening information on the facade

Bayesian Approaches for Modelling Flood Damage Processes

Hochwasserschadensprozesse werden von den drei Komponenten des Hochwasserrisikos bestimmt – der Gefahr, der Exposition und der Vulnerabilität. Dabei bleiben wichtige Einflussgrößen auf die Vulnerabilität, wie die private Hochwasservorsorge aufgrund fehlender quantitativer Informationen unberücksichtigt. Diese Arbeit entwickelt daher eine robuste statistische Methode zur Quantifizierung des Einflusses von privater Hochwasservorsorge auf die Reduzierung der Vulnerabilität von Haushalten bei Hochwasser. Es konnte gezeigt werden, dass in Deutschland private Hochwasservorsorgemaßnahmen den durchschnittlichen Hochwasserschaden pro Wohngebäude um 11.000 bis 15.000 Euro reduzieren. Hochwasserschadensmodelle mit Expertenwissen und datengestützten Methoden sind dabei am besten in der Lage Unterschiede in der Vulnerabilität durch private Hochwasservorsorge zu erkennen. Die über Hochwasserschadenprozesse erhobenen Daten und Modellannahmen sind von Unsicherheit geprägt und so sind auch Schätzungen mit. Die Bayesschen Modelle, die in dieser Arbeit entwickelt und angewandt werden, nutzen Annahmen über Schadensprozesse als Prior und empirische Daten zur Aktualisierung der Wahrscheinlischkeitsverteilungen. Die Modelle bieten Hochwasserschadensschätzungen als Verteilung, welche die Bandbreite der Variabilität der Schadensprozesse und die Unsicherheit der Modellannahmen abbilden. Hochwasserschadensmodelle, hinsichtlich der Prognoseerstellung und Anwendbarkeit. Ins Besondere verbessert die Verwendung einer Beta–Verteilung die Zuverlässigkeit der Modellergebnisse im Vergleich zu den häufig genutzten Gaußschen oder nicht parametrischen Verteilungen. Der hierarchische Bayessche Ansatz schafft eine verbesserte Parametrisierung von Wasserstand-Schadens-Funktionen und ersetzt so die Notwendigkeit empirischer Daten durch regional- und Ereignis-spezifisches Expertenwissen. Auf diese Weise kann die Vorhersage bei einer zeitlich und räumlichen Übertragung des Models verbessert werden.Flood damage processes are influenced by the three components of flood risk - hazard, exposure and vulnerability. In comparison to hazard and exposure, the vulnerability component, though equally important is often generalized in many flood risk assessments by a simple depth-damage curve. Hence, this thesis developed a robust statistical method to quantify the role of private precaution in reducing flood vulnerability of households. In Germany, the role of private precaution was found to be very significant in reducing flood damage (11 - 15 thousand euros, per household). Also, flood loss models with structure, parameterization and choice of explanatory variables based on expert knowledge and data-driven methods were successful in capturing changes in vulnerability, which makes them suitable for future risk assessments. Due to significant uncertainty in the underlying data and model assumptions, flood loss models always carry uncertainty around their predictions. This thesis develops Bayesian approaches for flood loss modelling using assumptions regarding damage processes as priors and available empirical data as evidence for updating. Thus, these models provide flood loss predictions as a distribution, that potentially accounts for variability in damage processes and uncertainty in model assumptions. The models presented in this thesis are an improvement over the state-of-the-art flood loss models in terms of prediction capability and model applicability. In particular, the choice of the response (Beta) distribution improved the reliability of loss predictions compared to the popular Gaussian or non-parametric distributions; the Hierarchical Bayesian approach resulted in an improved parameterization of the common stage damage functions that replaces empirical data requirements with region and event-specific expert knowledge, thereby, enhancing its predictive capabilities during spatiotemporal transfer

Altura y densidad urbana admisible de edificaciones multifamiliares representativa en un tramo de una avenida principal en Lima- Perú para la Gestión Prospectiva del Riesgo Sísmico en zonas residenciales

La evolución es intrínseca en el ser humano y con nosotros evoluciona nuestro entorno. La industria de la construcción a manos de la necesidad del hombre, busca no solo un crecimiento horizontal territorial, sino también uno vertical. Sin embargo, esta proyección en las alturas se limita a una gestión plenamente espacial con poco interés en la capacidad de tránsito y zonas de refugio del agente humano durante un evento sísmico. Esta investigación realiza una evaluación de un escenario real existente del tramo de la avenida en estudio y el análisis de escenarios hipotéticos con variación del ancho de la zona de refugio. De este modo, se propone valores límite de altura y densidad poblacional de edificaciones para una Gestión Prospectiva del Riesgo Sísmico teniendo como ejemplo base un tramo de la Av. San Felipe en Lima-Perú. Se realizó el análisis de una evacuación eficiente de las edificaciones hacia la zona de refugio con modelos basados en el agente con el software Pathfinder, modelos tridimensionales (BIM), se analizó tiempos de evacuación, la influencia de las edificaciones, comportamiento humano y los parámetros urbanísticos zonales. Los resultados del presente trabajo establecen gráficamente la pertinente relación directa de la altura máxima permisible de los edificios proyectados con el ancho de la zona de refugio para una correcta evacuación. Como ejemplo se tiene un tramo de la Av. San Felipe con 7m de ancho de la zona de refugio correspondiente a 18 pisos como altura límite en los 10 edificios adyacentes considerando una longitud frontal total de 144m.Evolution is intrinsic to human beings and our environment evolves with us. The construction industry at the hands of man's need, seeks not only a territorial horizontal growth, but also a vertical one. However, this projection in the heights is limited to a fully spatial management with little interest in the transit capacity and refuge areas of the human agent during a seismic event. This research carries out an evaluation of a real existing scenario of the section of the avenue under study and the analysis of hypothetical scenarios with variation in the width of the refuge area. In this way, limit values ​​of height and population density of buildings are proposed for a Prospective Management of Seismic Risk, taking as a base example a section of Av. San Felipe in Lima-Peru. The analysis of an efficient evacuation of the buildings towards the refuge area was carried out with models based on the agent with the Pathfinder software, three-dimensional models (BIM), evacuation times, the influence of the buildings, human behavior and the parameters were analyzed. zonal urban planning. The results of the present work graphically establish the pertinent direct relationship of the maximum permissible height of the projected buildings with the width of the refuge area for a correct evacuation. As an example, there is a section of Av. San Felipe with a 7m width of the refuge area corresponding to 18 floors as the limit height in the 10 adjacent buildings considering a total frontal length of 144m.Tesi

A real time urban sustainability assessment framework for the smart city paradigm

Cities have proven to be a great source of concerns on their impact on the world environment and ecosystem. The objective, in a context where environmental concerns are growing rapidly, is no longer to develop liveable cities but to develop sustainable and responsive cities. This study investigates the currently available urban sustainability assessment (USA) schemes and outlines the main issues that the field is facing. After an extensive literature review, the author advocates for a scheme that would dynamically capture urban areas sustainability insights during their operation, a more user-centred and transparent scheme. The methodological approach has enabled the construction of a solid expertise on urban sustainability indicators, the essential role of the smart city and the Internet of Thing for a real-time key performance indicators determination and assessment, and technical and organisational challenges that such solution would encounter. Key domains such as sensing networks, remote sensing and GIS technologies, BIM technologies, Statistical databases and Open Governmental data platform, crowdsourcing and data mining that could support a real-time urban sustainability assessment have been studied. Additionally, the use of semantic web technologies has been investigated as a mean to deal with sources heterogeneity from diverse data structures and their interoperability. An USA ontology has been designed, integrating existing ontologies such as SSN, ifcOWL, cityGML and geoSPARQL. A web application back-end has then been built around this ontology. The application backbone is an Ontology-Based Data Access where a Relational Database is mapped to the USA ontology, enabling to link sensors data to pieces of information on the urban environment. Overall, this study has contributed to the body of knowledge by introducing an Ontology-Based Data Access (OBDA) approach to support real-time urban sustainability assessment leveraging sensors networks. It addresses both technical and organisational challenges that the smart systems domain is facing and is believed to be a valuable approach in the upcoming smart city paradigm. The solution proposed to tackle the research questions still faces some limitations such as a limited validation of the USA scheme, the OBDA limited intelligence, an improvable BIM and cityGML models conversion to RDF or the lack of user interface. Future work should be carried out to overcome those limitations and to provide stakeholders a high-hand service