How can new technologies help us with earthquake reconnaissance?

Earthquake reconnaissance missions have been very successful in identifying the specific causes of failure for individual buildings and the deficiencies in building codes or local construction practices that have led to these; however, their ability to capture robust statistics on patterns of failure is usually beyond their scope. Furthermore, the success of these endeavours in establishing poor construction designs and practices, means that if we are to continue to learn new lessons we will need to gain fresh insights using new data streams. Recent technological advances have the ability to enable us to both increase the amount of data collected and to improve on the precision of these measurements. Furthermore, social media has the potential to provide entirely new data streams and to significantly add value to collected data by harnessing an army of data manipulators and interpreters. Howto do this in a reliable way however, is the subject of much debate. In this paper, we explore the potential for a number of trialled and potential technologies to collect better and new information in earthquake reconnaissance, including virtual damage surveying - where results from damage surveys completed in the field, are compared to omnidirectional images collected during the mission and interpreted by a virtual surveyor based in the UK, data collected through aerial images taken by UAVs and 3D models created from a series of drone or other images. Finally, we describe the potential of social media such as Twitter to collect data streams on damage and other impacts. Examples of impact data such as road closures, landslips and infrastructure service failures collected for flooding and landslide will be presented to show the potential of this technology for earthquakes

Ontology-based discovery of time-series data sources for landslide early warning system

YesModern early warning system (EWS) requires sophisticated knowledge of the natural hazards, the urban context and underlying risk factors to enable dynamic and timely decision making (e.g., hazard detection, hazard preparedness). Landslides are a common form of natural hazard with a global impact and closely linked to a variety of other hazards. EWS for landslides prediction and detection relies on scientific methods and models which requires input from the time series data, such as the earth observation (EO) and urban environment data. Such data sets are produced by a variety of remote sensing satellites and Internet of things sensors which are deployed in the landslide prone areas. To this end, the automatic discovery of potential time series data sources has become a challenge due to the complexity and high variety of data sources. To solve this hard research problem, in this paper, we propose a novel ontology, namely Landslip Ontology, to provide the knowledge base that establishes relationship between landslide hazard and EO and urban data sources. The purpose of Landslip Ontology is to facilitate time series data source discovery for the verification and prediction of landslide hazards. The ontology is evaluated based on scenarios and competency questions to verify the coverage and consistency. Moreover, the ontology can also be used to realize the implementation of data sources discovery system which is an essential component in EWS that needs to manage (store, search, process) rich information from heterogeneous data sources

Law in the present future : approaching the legal imaginary of smart cities with science (and) fiction

This doctoral research concerns smart cities, describing digital solutions and social issues related to their innovative technologies, adopted models, and major projects around the world. The many perspectives mentioned in it were identified by online tools used for the textual analysis of two databases that were built from relevant publications on the main subject by authors coming from media and academia. Expected legal elements emerged from the applied process, such as privacy, security, transparency, participation, accountability, and governance. A general review was produced on the information available about the public policies of Big Data in the two municipal cases of Rio de Janeiro and Montréal, and their regulation in the Brazilian and Canadian contexts. The combined approaches from science and literature were explored to reflect on the normative concerns represented by the global challenges and local risks brought by urban surveillance, climate change, and other neoliberal conditions. Cyberpunk Science Fiction reveals itself useful for engaging with the shared problems that need to be faced in the present time, all involving democracy. The results achieved reveal that this work was, in fact, about the complex network of practices and senses between (post)modern law and the imaginary of the future.Cette recherche doctorale centrée sur les villes intelligentes met en évidence les solutions numériques et les questionnements sociétaux qui ont trait aux technologies innovantes, ainsi qu’aux principaux modèles et projets développés autour d’elles à travers le monde. Des perspectives multiples en lien avec ces développements ont été identifiées à l’aide d’outils en ligne qui ont permis l’analyse textuelle de deux bases de données comprenant des publications scientifiques et des écrits médiatiques. De ce processus analytique ont émergé des éléments juridiques relatifs aux questions de vie privée, de sécurité, de transparence, de participation, d’imputabilité et de gouvernance. De plus, à partir de ces informations a été réalisée une revue des politiques publiques relatives aux mégadonnées dans les villes de Rio de Janeiro et de Montréal, ainsi que des réglementations nationales du Canada et du Brésil en lien avec ce sujet. Finalement, à travers l’exploration d’écrits scientifiques et fictionnels de la littérature, les principaux enjeux normatifs soulevés localement et mondialement par la surveillance urbaine, les changements climatiques et les politiques néolibérales ont pu être mis à jour. Le courant cyberpunk de la science-fiction s’est avéré particulièrement utile pour révéler les principaux problèmes politiques, en lien avec la préservation de la démocratie, auxquelles sont confrontées nos sociétés présentement. Les résultats de la recherche démontrent finalement la présence d’un réseau de pratiques et de significations entre le droit (post)moderne et les représentations imaginaires du futur

Innovative big data integrationand analysis techniques for urban hazard management

PhD ThesisModern early warning systems (EWS) require sophisticated knowledge of natural hazards, the urban context and underlying risk factors to enable dynamic and timely decision making (e.g., hazard detection, hazard preparedness). Landslides are a common form of natural hazard with a global impact and are closely linked to a variety of other hazards. EWS for landslide prediction and detection relies on scienti c methods and models which require input from the time-series data, such as the earth observation (EO) and ancillary data. Such data sets are produced by a variety of remote sensing satellites and Internet of Things sensors which are deployed in landslide-prone areas. Besides, social media-based time-series data has played a signi cant role in modern disaster management. The emergence of social media has led to the possibility of the general public contributing to the monitoring of natural hazard by reporting incidents related to hazard events. To this end, the data integration and analysis of potential time-series data sources in EWS applications have become a challenge due to the complexity and high variety of data sources. Moreover, sophisticated domain knowledge of natural hazards and risk management are also required to enable dynamic and timely decision making about serious hazards. In this thesis, a comprehensive set of algorithmic techniques for managing high varieties of time series data from heterogeneous data sources is investigated. A novel ontology, namely Landslip Ontology, is proposed to provide a knowledge base that establishes the relationship between landslide hazard and EO and ancillary data sources to support data integration for EWS applications. Moreover, an ontology-based data integration and analytics system that includes human in the loop of hazard information acquisition from social media is proposed to establish a deeper and more accurate situational awareness of hazard events. Finally, the system is extended to enable an interaction between natural hazard EWS and electrical grid EWS to contribute to electrical grid network monitoring and support decision-making for electrical grid infrastructure management