CityUHK Research Center
A CityUHK research center advancing intelligent built environments through AI, sensing, BIM, digital twins, and robotics.
Explore Research
Built-informatics and Smart Cities Center is grounded in the Department of Architecture and Civil Engineering at City University of Hong Kong and connects that academic base to a wider cross-disciplinary research network.
Its work brings together intelligent construction, built-environment sensing, BIM, digital twins, AI systems, and robotics so research can move from method development toward real operational settings.
The Center is positioned as an academic platform: it supports research collaboration, talent development, and translation without collapsing into either a purely administrative unit or a commercial product story.
From field capture to decision execution, this track focuses on Safety GPT, risk recognition, video-based site inspection, and automated reporting.
This track integrates scans, point clouds, imagery, and BIM/CAD models for progress tracking, scan-vs-BIM comparison, automated analysis, and reporting.
This track develops handheld scanning, SLAM, point-cloud segmentation, and pipe image segmentation for urban gas infrastructure mapping and maintenance.
This track develops self-built hardware, multi-terminal integration, and autonomous indoor/outdoor navigation for robotic construction and maintenance systems.
City University of Hong Kong
Department of Architecture and Civil Engineering
City University of Hong Kong
Department of Architecture and Civil Engineering
City University of Hong Kong
Department of Computer Science
City University of Hong Kong
Department of Computer Science
City University of Hong Kong
Department of Decision Analytics and Operations
The University of Hong Kong
Department of Computer Science
City University of Hong Kong
Department of Systems Engineering
An AI system for safety inspection, risk recognition, and automated reporting in construction.
A multimodal system for scan-to-BIM comparison, progress analysis, and documentation.
A full-stack platform for scanning, identifying, and operating urban gas infrastructure.
Self-developed robotic devices and navigation algorithms for urban construction and maintenance.
Representative deployment context connected to large-scale construction intelligence workflows.
Representative deployment context connected to multimodal progress intelligence.
Representative deployment context connected to robotics and large-scale built-environment workflows.
Representative Shenzhen-side deployment context connected to infrastructure digitization and operations.
A recent safety-focused journal article that directly supports the Center's smart-construction and risk-recognition direction.
This review anchors the Center's scan-to-BIM, digital-twin, and progress-intelligence workflow framing.
A representative project-organizing output showing how inter-organizational collaboration and knowledge flow matter in delivery settings.
A built-environment review that broadens the profile from smart construction into building-system decarbonization pathways.
The current Center structure presents Safety GPT 1.0 as a representative system for site inspection, risk recognition, and automated reporting within the construction-safety AI agenda.
The multimodal data-fusion track continues to frame scan-to-BIM comparison, progress analysis, and documentation as a central systems pathway for the Center.
The current project portfolio uses the Shenzhen Ecological Smart Valley Project as a Shenzhen-side practice reference for infrastructure digitization, operations, and cross-node collaboration.
Use the Hong Kong academic affiliation and official email as the primary public contact route for the Center.
Department of Architecture and Civil Engineering, City University of Hong Kong
