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In the context of disaster management, geospatial information plays a crucial role in the decision-making process to protect and save the population. Gathering a maximum of information from different sources to oversee the current situation is a complex task due to the diversity of data formats and structures. Although several approaches have been designed to integrate data from different sources into an ontology, they mainly require background knowledge of the data. However, non-standard data set schema (NSDS) of relational geospatial data retrieved from e.g. web feature services are not always documented. This lack of background knowledge is a major challenge for automatic semantic data integration. Focusing on this problem, this article presents an automatic approach for geospatial data integration in NSDS. This approach does a schema mapping according to the result of an ontology matching corresponding to a semantic interpretation process. This process is based on geocoding and natural language processing. This article extends work done in a previous publication by an improved unit detection algorithm, data quality and provenance enrichments, the detection of feature clusters. It also presents an improved evaluation process to better assess the performance of this approach compared to a manually created ontology. These experiments have shown the automatic approach obtains an error of semantic interpretation around 10% according to a manual approach.

Purpose The purpose of this paper is to improve the safety of construction workers by understanding their behaviors on construction sites using spatio-temporal (ST) trajectories. Design/methodology/approach A review of construction safety management literature and international occupational health and safety statistics shows that the major reasons for fatalities on construction sites are mobility-related issues, such as unsafe human behaviors, difficult site conditions, and workers falling from heights and striking against or being struck by moving objects. Consequently, literature has been reviewed to find possible technological solutions to track the mobility of construction workers to reduce fatalities. This examination has suggested that location acquisition systems, such as Global Positioning System (GPS), have been widely used for real-time monitoring and tracking of workers on construction sites for hazard prevention. However, the raw data captured from GPS devices are generally available as discrete points and do not hold enough information to understand the workers’ mobility. As a solution, an application to transform raw GPS data into ST trajectories using different preprocessing algorithms is proposed for enhancing worker safety on construction sites. Findings The proposed system preprocesses raw GPS data for stay point detection, trajectory segmentation and intersection of multiple trajectories to find significant places and movements of workers on a construction site to enhance the information available to H&S managers for decision-making processes. In addition, it reduces the size of trajectory data for future analyses. Originality/value Application of location acquisition systems for construction safety management is very well addressed in the existing literature. However, a significant gap has been found: the usage of preprocessed ST trajectories is still missing in workers’ safety monitoring scenarios in the area of construction management. To address this research gap, the proposed system uses preprocessed ST trajectories to monitor workers’ movements on a construction site to identify potentially unsafe behaviors.

Compliance checking for building models, cities and territories involve formalizing a set of model schema knowledge and constraint. The objective of our study is to propose: an information model to federate heterogeneous data sources describing an urban area (building and building environment) along with a method for formally specifying of urban rules. The overall goal we pursue is to be able to query and to verify data against different regulations and/or requirements. The purpose of this article is to describe our approach for interoperability among different data sources (e.g. IFC, CityGML) thus creating a consistent description of an urban area., La vérification de conformité des modèles de bâtiments, villes et territoires passe par la formalisation d’un ensemble de connaissances, de schémas de modèles et de contraintes. L’objectif de notre étude est de proposer : un modèle d’information permettant de fédérer des sources de données hétérogènes décrivant une zone urbaine (bâtiment, quartier, etc.) et une méthode de formalisation des règles urbaines, afin de pouvoir effectuer l’interrogation et la vérification des données au regard de différentes exigences règlementaires. Le but de cet article est de proposer une approche qui permet d’implémenter une interopérabilité entre les différentes sources de données (ex. IFC, CityGML), dans le but de créer une description cohérente d'une zone urbaine.

International audience; The term " disaster management " comprises both natural and man-made disasters. Highly pervaded with various types of sensors, our environment generates large amounts of data. Thus, big data applications in the field of disaster management should adopt a modular view, going from a component to nation scale. Current research trends mainly aim at integrating component, building, neighborhood and city levels, neglecting the region level for managing disasters. Current research on big data mainly address smart buildings and smart grids, notably in the following areas: energy waste management, prediction and planning of power generation needs, improved comfort, usability and endurance based on the integration of energy consumption data, environmental conditions and levels of occupancy. This paper aims presenting a systematic literature review on the applications of big data in disaster management. The paper will first presents the visual definition of disaster management and describes big data; it will then illustrate the findings and gives future recommendations after a systematic literature review.

International audience; Embedded computing systems, sensor networks, LBS pervasive deployment environments, and worldwide computing systems have common characteristics. They are large scale, decentralized and dynamic networks, and needing context-awareness to automatically adapt their behavior and continue their execution despite network dynamics. Identifying innovative software engineering approaches that take into account all the above mentioned characteristics is a real challenge. This paper focuses on LBS applications and the middleware models required for supporting their operation and characteristics.