Subventions et des contributions :

Titre :
Knowledge Discovery in 3D Geospatial Spaces: Towards an Integrated BIM, 3D GIS and IoT Data Framework
Numéro de l’entente :
RGPIN
Valeur d'entente :
100 000,00 $
Date d'entente :
10 mai 2017 -
Organisation :
Conseil de recherches en sciences naturelles et en génie du Canada
Location :
Ontario, Autre, CA
Numéro de référence :
GC-2017-Q1-02790
Type d'entente :
subvention
Type de rapport :
Subventions et des contributions
Informations supplémentaires :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

Nom légal du bénéficiaire :
Jadidi Mardkheh, Amaneh (Mojgan) (Université York)
Programme :
Programme de subventions à la découverte - individuelles
But du programme :

The world is entering the period of smart cities. The Internet of Things (IoT) and information-technology infrastructure are fundamental to smart cities, a network of connected smart devices, sensors, and big-data analytics. By 2020, estimates are that there will be 80 billion connected devices worldwide and over 5 billion Internet users. Connectivity is inevitable and will include all facets of our lives, seamlessly bringing together work, home and surrounding environments. In this context, 3D virtual city space is key to helping smart cities manage and monitor indoor-outdoor dynamics. Canadian cities are determined to achieve the goal of becoming smart cities. To foster dynamic city applications such as business growth and solutions for monitoring infrastructure, Toronto and Montreal have both launched 3D open data to the public. However, further research is required to deal with multi-scale analysis and visualization in a virtual space, extended from city-scale to building structures and vice-versa seamlessly through the temporal scale.
The main goal of this research is to increase our knowledge of how a variety of data coming from Building Information Model (BIM), 3D Geographical Information Systems (GIS) and IoT, integrate into a single environment to conduct spatiotemporal analytics. Our research will advance knowledge by identifying ontology, semantics and the geometry definition of integrated data through a multidimensional graph-structure. Nine HQP will be trained by working on the development of 1) A comprehensive, integrated data modeling framework handling BIM, GIS and IoT stream data; 2) An analytical approach for data mining in 3D indoor-outdoor virtual model in order to discover knowledge; 3) Spatial aggregation operations for multi-scale representation of discovered knowledge in a seamless indoor-outdoor virtual model; 4) A geospatial data sharing, interoperability and quality assessment framework for the integration of BIM, GIS and IoT.
Knowledge gained from our research will enable new technologies that have an aggregated and granulated image of massive amounts of 3D indoor-outdoor data through a virtual space connected with IoT stream information. This knowledge will have a significant impact on the Canadian geospatial industry sectors. Our research will also provide technological advancement, innovative approaches and deep insight into the standardization of 3D data sharing. The results will be applied to a wide range of smart cities applications such as energy efficiency, emergency management, infrastructure monitoring, and facility management.