Urban Informatics for Smart Cities
Urban informatics enables better use of the latest urban science theories in the development of cutting-edge urban sensing and computing technologies, which in turn helps better solve specific urban problems. An interview with Prof. Shi, President of the International Society for Urban Informatics and Editor-in-Chief of the journal Urban Informatics.
How does the open access journal Urban Informatics play a role with SDG 11?
To pursue SDG 11 involves various sectors in the cities as well as numerous science and technology issues. The transdisciplinary approach of urban informatics can bridge the gaps between disciplines and sectors. For example, urban informatics enables better use of the latest urban science theories and understanding in domain urban problems in the development of cutting-edge urban sensing and computing technologies, and, in turn, it can use the enhanced technologies to better solve specific urban problems. When the advances in efficiency and capacity of cities encounter problems such as environmental impact and social exclusion, urban informatics can also help better balance different considerations and generate sustainable solutions. As the first international journal dedicated to the field of urban informatics, this journal will help accumulate the knowledge for this emerging field, and, further, to stimulate the development and application of urban informatics by worldwide scholars and professionals, thereby equipping cities with better sustainable solutions.
What is the focus of your research work?
In the areas of urban informatics for smart cities, geographic information science and remote sensing, my research interests covers artificial-intelligence-based object extraction and change detection from satellite imagery, intelligent analytics and quality control for spatial big data, and mobile mapping and 3-D modelling based on LiDAR and remote sensing imagery.
What are the short- and long-term goals of your work?
The short-term goal of my work is to develop new technologies for supporting the development of smart and sustainable cities. In the long run, my team and I strive to transfer our technologies to practice and to promote the development of urban informatics in academia and society (e.g., through the journal Urban Informatics and the International Society for Urban Informatics), to make cities smarter and more sustainable.
Which SDGs does your work most closely relate to?
SDG 11 (sustainable cities and communities). Here are three examples:
(1) Smart city platform and mobile mapping for building digital twins and metaverse. Digital twins can be used for precise operation, simulation, and prediction of urban systems (e.g., intelligent transportation system), to make cities more efficient and enable better planning of sustainable cities. Metaverse is greatly enriching peoples experiences and services received through the immersive Internet environment, moreover, it is making these experiences and services more accessible for different social groups in general. Digital twins and metaverse require advanced digital representations of the city, and the Smart City Platform developed by my team serves this purpose (Figure 1).
Figure 1. Physical world (left) and corresponding digital models in the smart city platform (right) built by Prof. Shis team.
Incorporating our technologies of 3D city modeling including Building Information Modelling (BIM), AI-based urban object cognition, and spatial big data analytics, the platform can be used to create high-precision smart city data infrastructure and perform various analytics and simulations. To construct 3D city models on the platform, we also developed a lightweight mobile mapping backpack to map the real world with our spatial data capture methods (Figure 2).
Figure 2. As the operator walks carrying the mobile mapping backpack (lower-left), LiDAR (laser scanning) point cloud data (upper-right) is simultaneously captured for building 3D digital city models.
(2) Artificial intelligence (AI) methods for disaster and pollution mapping. We have developed a series of AI-based algorithms to recognize urban disasters and air pollution from remote sensing imagery and multi-source supporting data. Our AI-based landslide recognition methods are accurate and highly automated, and can extract landslide trails as well as detailed landslide attributes. Our landslides recognition system is being used by local governments for landslide mapping and formulating subsequent treatment plans.
(3) COVID-19 symptom onset prediction to improve urban resilience to the pandemic; this will be detailed in the last question.
What do you believe are the most effective ways of communicating your research?
Rapid publication, especially through open access journals, is the most effective way for communicating with academia. Webinars are excellent for reaching out to a wider audience. For example, in March 2022 the first webinar on Urban Informatics hosted by the International Society for Urban Informatics attracted around 200,000 viewers on various streaming platforms. The introduced the framework of urban informatics comprising urban science, urban big data infrastructure, urban sensing, urban computing, and urban systems and applications.
How, if at all, has your research shifted given the impacts of the COVID-19 pandemic? What are the trends youve noticed within your field?
I have put much research effort into spatial analytics for COVID-19. My team developed a series of spatiotemporal models for short-term prediction of COVID-19 symptom onset risk. We used the models to predict the epidemic risk and evaluate different epidemic control measures, to provide reference for precise control of COVID-19. We also publish our onset risk prediction results on our platform to inform the public (Figure 3). Dr. Gauden Galea, WHO Representative for China, shared our study on Omicron with the WHO Western Pacific Regional Office which serves 1.9 billion people and commented: this is an important contribution to our understanding (of Omicron) and having access to your findings is much appreciated; it is already of great use.
Figure 3. COVID-19 Symptom onset risk prediction result on our platform and actual symptom onset cases on 13 Apr 2021.
As for the research trend, I am glad to see that latest spatial big data and more advanced data analytics have been used to study COVID-19, which have greatly contributed to more reliable reference to public health policies and more effective treatments. Researchers also have been studying the new trends of human spatial behaviors in the post-pandemic cities, which is undoubtedly also very meaningful.
Take a closer look at the open access journal . We also invite you to visit our hub for SDG 11 Sustainable Cities and Communities.
About the Author:
Professor Wenzhong (John) Shi is currently the Director of PolyU-Shenzhen Technology and Innovation Research Institute (Futian), Director of Otto Poon Charitable Foundation Smart Cities Research Institute of PolyU, Chair Professor in Geographic Information Science and Remote Sensing, and Director of Joint Research Laboratory on Spatial Information of PolyU and Wuhan University. He serves as President of the International Society for Urban Informatics and Editor-in-Chief of the OA journal Urban Informatics.
