PolyU develops a range of advanced patented technologies to expedite smart city development in Hong Kong

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HONG KONG, April 28, 2021 /PRNewswire/ -- In the "Hong Kong Smart City Blueprint 2.0" released in 2020, the Hong Kong government put forward a number of measures to capitalise on innovation and technology (I&T) to build a world-famous Smart Hong Kong characterised by a strong economy and high quality of living. Under this backdrop, The Hong Kong Polytechnic University (PolyU) established the Smart Cities Research Institute (SCRI) in 2020. As Director of SCRI, Professor Wenzheng SHI led his team to develop a number of cutting-edge patented technologies that help address various societal issues, including the revitalisation of old buildings, slope safety, prevention and control of the COVID-19 pandemic, and the construction of spatial data infrastructure, hoping to provide comprehensive solutions for smart city development in Hong Kong and the Nation. The project received a Gold Medal at this year's Special Edition 2021 Inventions Geneva Evaluation Days – Virtual Event. The following three PolyU-developed smart city technologies by Professor Shi's team are vital in promoting smart city development:

Three-dimensional (3D) Mobile Mapping System: Providing accurate 3D maps to support wide smart city applications

According to the statistics in 2019, more than 10,000 old residential buildings are at least 50 years old in Hong Kong. Most of these old buildings do not have 3D indoor Building Information Models (BIM), which creates many challenges when it comes to reconstruction or maintenance. In view of this, the PolyU team has developed a lightweight and reliable 3D mobile mapping system (mobile mapping backpack), which can easily measure cities and obtain 3D maps with centimeter-level accuracy. It can be used to build spatial data infrastructure and can thus support smart city applications in many fields. The system adopts advanced technologies such as Simultaneous Localisation and Mapping (SLAM), which is not restricted by the signal receiving area of the Global Navigation Satellite System (GNSS). It can carry out continuous data collection in different complex indoor and outdoor environments and is particularly suitable for high-density and complex urban environments, such as those in Hong Kong. 

Modular Integrated Construction (MiC) is a primary development direction of today's construction industry. However, due to the large size of the modular components, the vehicles carrying these components have difficulties passing through some road sections in the urban areas of Hong Kong. To address the issue, the PolyU team worked in collaboration with the Hong Kong Construction Industry Council by making use of the mobile mapping backpack to conduct accurate 3D measurement of critical narrow road sections for identifying the location of obstacles, which could optimise the route for transporting oversized components without passing through narrow road sections. In addition, mobile mapping backpacks can be used to help obtain detailed indoor 3D models to support firefighting and provide evacuation routes for personnel at the fire scene.

AI-based Landslide Recognition: Reporting landslide and facilitating disaster control

Accurate and timely acquisition and update of spatial data infrastructure from remotely sensed big data is a long-standing challenge for smart city construction. Despite the increasingly widespread use of cutting-edge artificial intelligence (AI) technology in remote sensing object recognition, the accuracy and reliability of AI-based remote sensing object recognition still needs to be improved. The PolyU team has developed a series of AI-based algorithms to recognise various ground objects from remotely sensed data with higher accuracy and reliability, which supports the work of smart government in many areas such as urban planning and disaster mitigation.

The PolyU team developed a software system that integrates these AI algorithms to assist the Hong Kong Civil Engineering and Development Department to recognise landslides, a major natural disaster in Hong Kong. The software system can automatically and quickly recognise and locate landslides with an accuracy up to over 90%. It can also extract rich information such as the shape, area, height, and trail of the landslides. This provides important technical support for landslide control in Hong Kong.

Spatiotemporal Prediction of COVID-19 Onset Risk: To help public health agencies formulate more precise prevention and control strategies

The use of I&T in combating COVID-19 is a key Smart Living initiative in the Hong Kong Smart City Blueprint 2.0. Based on its long-term advantage in the field of spatiotemporal big data analytics, the team led by Professor Shi developed the extended Weighted Kernel Density Model for predicting the COVID-19 symptom onset risk. The model can be used to predict the spatiotemporal onset risk continuously. The prediction accuracy in the next three days can reach more than 85%. Focusing on predicting the risk of the symptom onset of cases, this model can predict the development trend of the epidemic in a timelier manner and support the public health department to formulate more precise prevention and control strategies. Based on the self-developed Spatiotemporal Big Data Platform, Professor Shi and his team have developed a visualization platform for COVID-19 onset risk to showcase the latest developments and short-term forecasts of the epidemic.

Professor John Shi said, "Smart cities is one of the key research focuses of the University. PolyU's multidisciplinary research capabilities in the fields of urban informatics, artificial intelligence, robotics and data science will help us further enhance the research of smart city technology. Looking ahead, the PolyU Smart City Research Institute will concentrate its research on a range of smart cities applications such as transportation, the environment, and ageing issues, so as to expedite the development of smart cities in Hong Kong and the Nation."

SOURCE Hong Kong Polytechnic University

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