3D Intelligent Spatial Modeling for Infrastructure Digital Twins

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In recent years, digital twin technology has been introduced as an emerging digital framework for infrastructure data, which creates a digital duplicate of physical infrastructure assets and their surrounding environment, processes, and systems. In this framework, the physical and virtual infrastructure models (physical twins) are tied together using data from Internet of Things (IoT) sensors (data twins), and enhanced using advanced data analytics technologies, such as artificial intelligence, machine learning, and predictive analytics (intelligence twins). This modelling, analysis, and simulation will power predictive asset maintenance, support planning decisions, and enable performance optimization of the nation's existing and future infrastructure. In this Discovery proposal, we propose a comprehensive approach to developing a novel 3D framework with potential applications towards infrastructure digital twins, urban sustainability, and smart city planning. By advancing our on-going research on augmented urban space modeling, we will develop innovative deep learning techniques to automatically reconstruct physical twins of infrastructure assets from visual sensory data, which will be used for application of emerging digital twin technologies. The objectives of this proposed program include: 1) developing a new graph-based deep learning method to enhance structured representation of contextual relations for scene segmentation; 2) developing a new deep learning method for reconstructing a generalized shape of building models with a combinatorial set of primitives; and 3) developing a new deep learning method for the inverse procedural modeling of trees. The outcomes of the proposed research have the potential to significantly advance and revolutionize infrastructure management techniques. The systems and approaches developed in this research program will result in the autonomous production of high-accuracy 3D modeling and the detailed semantic recognition of complex infrastructure assets, able to compete with the current human-centric methods of observation. This will be presented as a digital twin platform with the integration of sensor networks, data analytics, and simulation to improve infrastructure efficiency. The HQPs trained within this program will be able to transfer their knowledge and skills to various Canadian industries and contribute to the field of precision mapping via modern geo-spatial, digital twin and AI technologies. Our ultimate goal for this research program is to develop sound theories, which enable the reconstruction of generalized and interoperable 3D models of our infrastructure and environment within digital twin frameworks through perceptual and control processes, contributing to the creation of machine intelligence and the demonstration of working systems in large-scale engineering applications to support infrastructure sustainability and smart city.