Tag: Research Collaboration

  • Can Satellites Point the Way to More Liveable, Sustainable Cities?

    Can Satellites Point the Way to More Liveable, Sustainable Cities?

    In just 40 years, China went from a predominantly rural society to a highly urbanised one. What happened? And what lessons can other urbanising regions around the globe draw from that experience? 

    University of Hong Kong Professor of landscape architecture Bin Chen believes we can find the answers in satellite data. An expert in remote sensing and Director of HKU’s Future Urbanity and Sustainable Environment (FUSE) lab, he’s using satellite imagery to track everything from the historical growth of cities to emerging problem areas like urban heat, air pollution, and green/blue space loss. 

    It’s a cutting-edge field – and one with implications far beyond China. Done correctly, it could provide one of the first-ever windows into how cities grow and evolve in the real world. This emerging “urban intelligence” will in turn have major ramifications for rapidly urbanising countries across the Global South, allowing them to avoid mistakes made by other cities while empowering them to create more equitable built environments for all residents.  

    We asked Professor Chen to share his thoughts on the nature of his work, the importance of remote sensing and AI to urban planning, and how the world can build more equitable cities. 

    Professor Bin Chen points to some of his research on satellites and urban intelligence
    Professor Bin Chen points to some of his research on satellites and urban intelligence

    The “intangibles” of sustainability 

    Professor Chen characterises his work as looking at the “tangibles and intangibles” of the built environment.  

    The tangibles are relatively straightforward. But the intangibles – air, heat, sunlight, light pollution, shade, and noise – play just as important a role in our lives and health, while being much harder to quantify. 

    That’s where satellites can play a role, Professor Chen says, as new data and methods allow researchers to look at cities on a granular level, identifying problematic heat islands, light pollution, and air quality. 

    “It’s not just about climate. It’s also about liveability.” 

    – Professor Bin Chen

    Importantly, these are also problems of equity and ESG, with poorer residents often having to go without access to parks, shade, and clean air.  

    “Take shade, for example,” Professor Chen says. “In a city of high-rises, where you have people living in subdivided units with no access to sunlight year-round, shade is a social issue.”  

    Even if every building in a given district meets regulations, it can be hard to tell what the overall outcome will be – a problem satellite imagery and AI modelling can help solve

    While Professor Chen’s team can identify the issue, he hopes work on the solutions will be an interdisciplinary affair. “We need to bring experts together,” he says. “We need knowledge from social economics, environmental studies, data science, and urban planning.” 

    Professor Bin Chen poses with a sign for the Department of Landscape Architecture and HKU
    Professor Bin Chen poses with a sign for the Department of Landscape Architecture and HKU

    Urban intelligence 

    The long-term goal is to develop what Professor Chen calls “urban intelligence” – a deeper understanding how people, cars, buildings, and the environment interact – then apply that knowledge to developing regions around the world. 

    Perhaps surprisingly, given their importance in modern life, the actual mechanisms by which cities grow are not always well understood. It wasn’t until 2008, when satellite data became more widely accessible, and 2015, when large-scale cloud computing and machine learning caught up, that researchers could closely examine the growth of modern cities.  

    “AI makes everything more efficient.” 

    – Professor Bin Chen

    Professor Chen points to Shanghai’s Pudong New Area as a classic example, noting that while policy documents offer a window into its growth, remote sensing technology allows researchers a seamless, transparent view of how the district grew into a global financial capital. 

    Leading the way 

    But taking advantage of these advances will require more – and more open – data.  

    Still, researchers can make significant progress via virtual collaborations. A few years ago, Professor Chen worked with HKU Professor Peng Gong and partners from 23 universities and institutes across China on a database of Chinese urban land use.  

    By mixing satellite data with on-the-ground verification, they were able to create the country’s first nationwide parcel-level essential urban land use categories map, allowing researchers to easily compare cities around China, from Beijing to Shenzhen and Wuhan. 

    “Remote sensing is becoming more and more powerful. We used to have to focus on individual cities, but now we can look at entire countries, even the whole globe.” 

    – Professor Bin Chen

    That empowered numerous follow-up studies, and the team hopes to expand their map to the rest of the world in the coming years.  

    “We’re entering a new stage, going beyond remote sensing with multimodal data,” says Professor Chen. “But pushing the field forward will require more data sharing.” 

    • Professor Bin Chen, an expert on satellites, remote sensing, and urban intelligence, speaks to a student
    • Professor Bin Chen, an expert on satellites, remote sensing, and urban intelligence, poses in front of a sign for his Future Urbanity and Sustainable Environment Lab
    • Professor Bin Chen, an expert on satellites and urban intelligence, poses with some of his awards
  • Teaching Machines to Think Quantumly: Qi Zhao on the Frontier of AI-Driven Computing

    Teaching Machines to Think Quantumly: Qi Zhao on the Frontier of AI-Driven Computing

    “Quantum computers don’t just calculate. They learn from the rules of nature itself.” — Prof. Qi Zhao

    Artificial intelligence is everywhere — in our phones, our cities, and the tools we use to think.

    But for Professor Qi Zhao at The University of Hong Kong’s School of Computing and Data Science (CDS), the next leap in AI may come from a place far smaller than any silicon chip.

    His research explores how quantum physics and machine learning can work together to create a new kind of intelligence — one that learns the way the universe learns.


    From Theory to Computation

    Zhao trained as a quantum information theorist, studying how data behaves when stored in particles rather than bits.
    At HKU CDS, he leads a group that builds hybrid computing models combining classical algorithms with quantum processors.

    His goal is simple to state but hard to achieve: use quantum systems to make AI faster, smarter, and more energy-efficient.

    “Classical computers follow fixed paths,” he explains. “Quantum computers can explore many paths at once. That difference changes how learning works.”


    Reimagining Computation

    Traditional AI trains neural networks through repetition — adjusting parameters until patterns emerge.
    Quantum computers take a different approach.

    They rely on variational quantum algorithms, where a small quantum circuit learns by tuning itself with help from a classical controller.

    Think of it as teamwork: the quantum part handles exploration; the classical part handles evaluation. Together, they solve problems that would take ordinary machines far longer to compute. Zhao’s team studies how this cooperation could transform optimization tasks, from image recognition to material design.


    Quantum Machine Learning in Action

    Inside his lab, AI helps control fragile quantum hardware.

    Algorithms adjust pulse shapes, timing, and temperature to keep qubits stable. The system learns which conditions produce reliable results and adapts automatically when the environment changes. “It’s feedback learning in the truest sense,” Zhao says. “The machine is teaching itself how to stay coherent.”

    These experiments do more than improve performance. They show how AI and quantum physics can enhance each other.
    AI stabilizes quantum devices; quantum mechanics gives AI new mathematical tools for creativity and pattern discovery.


    Learning from Quantum Data

    Zhao believes that the next revolution will come when AI no longer just analyzes quantum data — it learns inside quantum data.

    His group explores models where quantum systems perform the learning directly, finding relationships hidden from classical logic.
    Such systems might recognize molecular structures or financial correlations beyond human intuition.

    “This is where AI stops imitating intelligence,” he explains. “It begins to share it.”


    Mentorship and Collaboration at CDS

    As a mentor, Zhao encourages students to cross boundaries between physics and computer science.
    He collaborates closely with Prof. Giulio Chiribella, Prof. Yuxiang Yang, and Prof. Ravi Ramanathan, creating a bridge between theory, experiment, and data science.

    In class, he simplifies complex formulas into visual intuition. His students learn not only to code algorithms but also to think about why an algorithm works. “The most exciting discoveries,” he says, “often happen when we try to explain them simply.”


    Looking Ahead: The Shape of Quantum Intelligence

    Zhao imagines a future where AI systems powered by quantum hardware design drugs, manage energy grids, or simulate ecosystems in real time.

    These machines will not replace human reasoning; they will extend it.

    “Intelligence isn’t just logic,” he says. “It’s the ability to learn from limited information. That’s what quantum mechanics has been doing for billions of years.”

    In his view, teaching machines to think quantumly is not just about computation — it’s about understanding learning itself.
    And at HKU CDS, that journey has already begun.