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Machine learning and natural language processing of social media data for event detection in smart cities / Andrei Hodorog in Sustainable Cities and Society, vol 85 (October 2022)  

Public [article]  inSustainable Cities and Society > vol 85 (October 2022) . - n° 104026 Titre : Machine learning and natural language processing of social media data for event detection in smart cities Type de document : Article/Communication Auteurs : Andrei Hodorog, Auteur ; Ioan Petri, Auteur ; yacine Rezgui, Auteur Année de publication : 2022 Article en page(s) : n° 104026 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] apprentissage automatique [Termes IGN] classification bayesienne [Termes IGN] détection d'événement [Termes IGN] données issues des réseaux sociaux [Termes IGN] outil d'aide à la décision [Termes IGN] régression multiple [Termes IGN] taxinomie [Termes IGN] traitement du langage naturel [Termes IGN] ville intelligente Résumé : (auteur) Social media data analysis in a smart city context can represent an efficacious instrument to inform decision making. The manuscript strives to leverage the power of Natural Language Processing (NLP) techniques applied to Twitter messages using supervised learning to achieve real-time automated event detection in smart cities. A semantic-based taxonomy of risks is devised to discover and analyse associated events from data streams, with a view to: (i) read and process, in real-time, published texts (ii) classify each text into one representative real-world category (iii) assign a citizen satisfaction value to each event. To select the language processing models striking the best balance between accuracy and processing speed, we conducted a pre-emptive evaluation, comparing several baseline language models formerly employed by researchers for event classification. A heuristic analysis of several smart cities and community initiatives was conducted, with a view to define real-world scenarios as basis for determining correlations between two or more co-occurring event types and their associated levels of citizen satisfaction, while further considering environmental factors. Based on Multiple Regression Analysis (MRA), we established the relationships between scenario variables, obtaining a variance of 60%–90% between the dependent and independent variables. The selected combination of supervised NLP techniques leverages an accuracy of 88.5%. We found that all regression models had at least one variable below the 0.05 threshold of the , therefore at least one statistically significant independent variable. These findings ultimately illustrate how citizens, taking the role of active social sensors, can yield vital data that authorities can use to make educated decisions and sustainably construct smarter cities. Numéro de notice : A2022-764 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scs.2022.104026 Date de publication en ligne : 02/07/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101785 [article]

Detecting spatiotemporal traffic events using geosocial media data / Shishuo Xu in Computers, Environment and Urban Systems, vol 94 (June 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 94 (June 2022) . - n° 101797 Titre : Detecting spatiotemporal traffic events using geosocial media data Type de document : Article/Communication Auteurs : Shishuo Xu, Auteur ; Songnian Li, Auteur ; Wei Huang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101797 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse de groupement [Termes IGN] base de données d'objets mobiles [Termes IGN] base de données spatiotemporelles [Termes IGN] détection d'événement [Termes IGN] données issues des réseaux sociaux [Termes IGN] données spatiotemporelles [Termes IGN] planification urbaine [Termes IGN] sécurité routière [Termes IGN] Toronto [Termes IGN] trafic routier [Termes IGN] Twitter Résumé : (auteur) Social media platforms enable efficient traffic event detection by allowing users to produce geo-tagged content (e.g., tweets) known as geosocial media data. Geosocial media data improve road safety by providing timely updates for traffic flow and traffic control. Recent studies on traffic event detection with geosocial media data have been focused around keyword-based query approaches, where the event content was inferred by predetermined categories, to retrieve relevant traffic events. Spatiotemporal features associated with traffic-related posts have not been fully investigated. In this study, we filtered irrelevant posts with association rules. A spatiotemporal clustering-based method was then used to retrieve traffic events from these filtered posts, where the content of detected events was automatically inferred with a set of representative terms. For comparison, a typical text classification-based method was also used by classifying the posts filtered from association rules into different categories. By validating the detection results with vehicle travel speed data, we demonstrate that the former outperforms the latter in terms of the number of correctly detected traffic events from one-year of Twitter data in Toronto, Canada. Our proposed approach helps organizations and governments to be aware of when and where traffic events occur by identifying event hotspots and peak periods, which improves both traffic management and urban planning. Numéro de notice : A2022-264 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101797 Date de publication en ligne : 26/03/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101797 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100261 [article]

A hierarchical model for semantic trajectories and event extraction in indoor and outdoor spaces / Hassan Noureddine (2022)  

Public Titre : A hierarchical model for semantic trajectories and event extraction in indoor and outdoor spaces Type de document : Thèse/HDR Auteurs : Hassan Noureddine, Auteur ; Christophe Claramunt, Directeur de thèse ; Cyril Ray, Directeur de thèse Editeur : Brest : Université de Bretagne Occidentale Année de publication : 2022 Importance : 113 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de l'Université de Bretagne Occidentale, spécialité Géomatique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] approche hiérarchique [Termes IGN] détection d'événement [Termes IGN] données spatiotemporelles [Termes IGN] espace intérieur [Termes IGN] espace urbain [Termes IGN] mobilité humaine [Termes IGN] modèle sémantique de données [Termes IGN] modélisation 3D [Termes IGN] ville intelligente Index. décimale : THESE Thèses et HDR Résumé : (auteur) The interest in exploiting crowd-sourced information has recently emerged as it can bring many valuable benefits for many application domains. This is particularly the case for realtime mobile crowd-sourcing data often available in indoor and outdoor environments. Such data offers many opportunities for analysing human mobility, especially when associated with multidimensional contextual information. Considering the diversity of multi-environment spaces and where mobility occurs, raises several data modelling, management and processing research challenges.When associated with multiple contextual information, indoor and outdoor mobility analysis stresses the need for appropriate and flexible data abstractions at the modelling level to represent the spatial, temporal and semantic data that arise in a smart city environment. While recent approaches often considered this issue using the common stops and moves model, this does not completely cover the multi-dimensional contextual information that arises in real-time on humans navigating through indoor and outdoor spaces. It also increases the need for computing systems and data architectures to process spatio-temporal data in a timely manner when searching for complex mobility events of interest. Despite the ability to represent spatio-temporal events, such systems require well-defined and flexible data manipulation languages to support abstraction and composition mechanisms for analysing urban mobilities.This thesis aims to provide the necessary constructs for analysing mobile crowd-sensed information that arises in indoor and outdoor spaces. In order to better understand urban mobility data in batch and real-time, we consider a broad range of contextual information that can be associated with mobility data. We introduce an indoor and outdoor spatial data model represented as a multi-layered graph and constructed with crowd-sourced trajectory data. The novelty of the approach lies in the fact that it provides a homogeneous and flexible hierarchical indoor and outdoor spatial model that can be associated with crowd-sensed trajectory data on the fly. Our modelling approach defines generic and flexible semantic trajectories considering multiple collaborative data semantics at different granularities and where trajectory segmentation relies on evolving semantic values. This thesis develops a modelling framework for complex events applied to our indoor and outdoor semantic trajectory model based on a formallanguage that establishes the required operations for the composition of the events. We have implemented data pipelines to examine our approach’s efficiency. The whole approach is experimented and applied to participatory data from a real case study to show its suitability, scalability and performance. Note de contenu : 1- Introduction 2- Related work 3- Semantic trajectory data model For indoor and outdoor spaces 4- Composite event extraction from stream of semantic trajectories 5- Conclusion Numéro de notice : 24080 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Géomatique : Brest : 2022 Organisme de stage : Institut de Recherche de l’Ecole Navale DOI : sans En ligne : https://theses.hal.science/tel-03888591 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102293

The point-descriptor-precedence representation for point configurations and movements / Amna Qayyum in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 7 (July 2021) . - pp 1374 - 1391 Titre : The point-descriptor-precedence representation for point configurations and movements Type de document : Article/Communication Auteurs : Amna Qayyum, Auteur ; Bernard De Baets, Auteur ; Muhammad Sulman Baig, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1374 - 1391 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] courbe [Termes IGN] détection d'événement [Termes IGN] données spatiotemporelles [Termes IGN] mesurage de distances [Termes IGN] objet mobile [Termes IGN] reconnaissance de formes [Termes IGN] relation topologique [Termes IGN] trafic routier [Termes IGN] véhicule automobile Résumé : (auteur) In this paper, we represent (moving) point configurations along a curved directed line qualitatively by means of a system of relational symbols based on two distance descriptors: one representing distance along the curved directed line and the other representing signed orthogonal distance to the curved directed line. The curved directed line represents the direction of the movement of interest. For instance, it could be straight as in the case of driving along a highway or could be curved as in the case of an intersection or a roundabout. Inspired by the Point Calculus, the order between the points on the curved directed line is described by means of a small set of binary relations () acting upon the distance descriptors. We call this representation the Point-Descriptor-Precedence-Static (PDPS) representation at a time point and Point-Descriptor-Precedence-Dynamic (PDPD) representation during a time interval. To illustrate how the proposed approach can be used to represent and analyse curved movements, some basic micro-analysis traffic examples are studied. Finally, we discuss some extensions of our work to highlight the practical benefits of PDP in identifying motion patterns that could be useful in GIS, autonomous vehicles, sports analytics, and gait analysis. Numéro de notice : A2021-453 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1864378 Date de publication en ligne : 11/01/2021 En ligne : https://doi.org/10.1080/13658816.2020.1864378 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97882 [article]

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Activity recognition in residential spaces with Internet of things devices and thermal imaging / Kshirasagar Naik in Sensors, vol 21 n° 3 (February 2021)  

Public [article]  inSensors > vol 21 n° 3 (February 2021) . - n° 988 Titre : Activity recognition in residential spaces with Internet of things devices and thermal imaging Type de document : Article/Communication Auteurs : Kshirasagar Naik, Auteur ; Tejas Pandit, Auteur ; Nitin Naik, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 988 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] compréhension de l'image [Termes IGN] contrôle par télédétection [Termes IGN] détection d'événement [Termes IGN] espace intérieur [Termes IGN] image RVB [Termes IGN] image thermique [Termes IGN] intelligence artificielle [Termes IGN] internet des objets [Termes IGN] itération [Termes IGN] modèle stéréoscopique [Termes IGN] objet mobile [Termes IGN] reconnaissance automatique [Termes IGN] reconnaissance d'objets [Termes IGN] scène 3D Résumé : (auteur) In this paper, we design algorithms for indoor activity recognition and 3D thermal model generation using thermal images, RGB images, captured from external sensors, and the internet of things setup. Indoor activity recognition deals with two sub-problems: Human activity and household activity recognition. Household activity recognition includes the recognition of electrical appliances and their heat radiation with the help of thermal images. A FLIR ONE PRO camera is used to capture RGB-thermal image pairs for a scene. Duration and pattern of activities are also determined using an iterative algorithm, to explore kitchen safety situations. For more accurate monitoring of hazardous events such as stove gas leakage, a 3D reconstruction approach is proposed to determine the temperature of all points in the 3D space of a scene. The 3D thermal model is obtained using the stereo RGB and thermal images for a particular scene. Accurate results are observed for activity detection, and a significant improvement in the temperature estimation is recorded in the 3D thermal model compared to the 2D thermal image. Results from this research can find applications in home automation, heat automation in smart homes, and energy management in residential spaces. Numéro de notice : A2021-159 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/s21030988 Date de publication en ligne : 02/02/2021 En ligne : https://doi.org/10.3390/s21030988 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97075 [article]

Techniques for efficient detection of rapid weather changes and analysis of their impacts on a highway network / Adil Alim in Geoinformatica, vol 24 n° 2 (April 2020)  

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Placial analysis of events: a case study on criminological places / Sunghwan Cho in Cartography and Geographic Information Science, Vol 46 n° 6 (November 2019)  

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Challenges in grassland mowing event detection with multimodal Sentinel images / Anatol Garioud (2019)  

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Joint analysis of SAR and optical satellite images time series for grassland event detection / Anatol Garioud (2019)

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Unsupervised detection of ruptures in spatial relationships in video sequences based on log‑likelihood ratio / Abdalbassir Abou-Elailah in Pattern Analysis and Applications, vol 21 n° 3 (August 2018)  

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