From heatwaves to pandemic diseases, the urban environments of the world face numerous challenges. Researchers at the Hong Kong Polytechnic University are harnessing artificial intelligence (AI) and informatics to address emerging concerns related to environmental changes and urban growth.
Innovative geospatial and AI technologies offer ground-breaking solutions and insights into the dynamic changes occurring in our natural and social surroundings. The applications of GeoAI are rapidly expanding across various fields, encompassing transportation, urban and public safety, planning, climate change and natural disasters.
Prof. Qihao WENG, Chair Professor of Geomatics and Artificial Intelligence of the Department of Land Surveying and Geo-Informatics, and Global STEM Professor, established the PolyU Research Centre for Artificial Intelligence in Geomatics (RCAIG), to focus on the development of original and innovative AI methodologies and technologies for geomatics and their applications in urban areas, with the goal of it becoming a global R&D hub in GeoAI. Prof. Weng has recently been honoured with the 2024 American Association of Geographers (AAG) Wilbanks Prize for Transformational Research in Geography and the 2024 AAG Remote Sensing Specialty Group Lifetime Achievement Honor Award for his ground-breaking contributions in geography.
Earth observations Prof. WENG said, “By leveraging the latest geospatial technology and AI, we stand at the forefront of addressing global environmental and societal challenges. Our research encompasses a wide spectrum of subjects in the fields of earth observations and geoinformatics.”
Satellite observations are invaluable tools for our community, relying on satellite imagery, videos and data that are crucial for informed decision-making in urban resilience and public health. For instance, satellite observations help us understand the impact of extreme heatwave on population exposure and aid in the development of urban flood monitoring algorithms. Real-time data acquisition also facilitate applications in traffic conditions, air quality, nature disasters, population movement and urban land use.
Prof. WENG said, “Earth observation is important as a guiding compass for understanding changes in the environment and society. Our research focuses on diverse fields including Geospatial big data and AI, remote sensing, ground-based sensors, navigation and positioning, surveying and geodesy, laser scanning and photogrammetry. These technologies play a crucial role in addressing and resolving key issues.”
In particular, GeoAI has revolutionised building monitoring by utilising thousands of learnable parameters. An illustration of this is its ability to automatically learn and identify general patterns of buildings such as colour and shape. This technology is crucially applied to detect disaster-damaged buildings, retrieve building height, identify structural changes, and estimate building energy consumption. As a result, GeoAI has emerged as a mainstream solution for more efficient and insightful building monitoring.
Environmental monitoring
As the world rapidly urbanises, cities become the focal point of diverse aspects of human development, including building and environmental monitoring, conservation efforts, urban safety, and the impacts of climate change.
By leveraging AI techniques like deep neural networks, alongside with remote sensing methods, these technologies have the ability to detect and track changes such as in habitats, urbanisation and deforestation patterns. Additionally, monitoring the uptake of carbon by vegetation plays a crucial role in combating climate change and developing effective mitigation strategies.
For urban resilience and public health, these technologies aim to enhance the ability of urban areas to withstand and recover from various challenges such as extreme heatwaves, while promoting the well-being and sustainable development of urban population.
In the field of urbanisation monitoring, research team of the RCAIG has developed an impervious surface area (ISA) based urban cellular automata (CA) model that can simulate the fractional change of urban areas within each grid by utilising annual urban extent time series data obtained from satellite observations. By characterising the historical pathways of urban area growth under different levels of urbanisation, the model offers more detailed insights compared to traditional binary CA models. This demonstrates its great potential in supporting sustainable development.
Research conducted by Ms Wanru HE, an RCAIG doctoral research assistant and the team, titled “Modeling gridded urban fractional change using the temporal context information in the urban cellular automata model” was published on Cities. Their model effectively capture the dynamics of urban sprawl with significantly improved computational efficiency and performance, and it enables the modelling of urban growth at regional even global level, under diverse future urbanisation scenarios.
GeoAI for traffic management
GeoAI utilises machine learning and deep learning to effectively analyse intricate information, offering applications like real-time traffic management. Through the integration of diverse data modalities, such as text, images, and knowledge graphs, GeoAI enables accurate traffic flow prediction, route optimisation, accident warnings, and the planning of an efficient traffic network. Consequently, this contributes to the advancement of smart traffic management.
To enhance the efficiency of ride-hailing platforms and achieve intelligent management of their services, research team of the RCAIG has developed a multi-agent order matching and vehicle repositioning (MAMR) approach. This innovative technology focuses on coordinating the supply and demand of ride-hailing services, ultimately aiming to improve their overall efficiency.
This approach provides a ground-breaking solution to tackle two critical aspects of efficient ride-hailing services. Firstly, it addresses order matching by efficiently assigning orders to available vehicles. Secondly, it incorporates proactive vehicle repositioning, strategically deploying idle vehicles to regions with potentially high demand. Based on multi-agent deep reinforcement learning (MARL), this innovation solves the complex planning in transportation and offers a news perspective on long-term spatiotemporal planning problem. The research conducted by Ms Mingyue XU, another RCAIG researcher and the team, titled “Multi-agent reinforcement learning to unify order-matching and vehicle-repositioning in ride-hailing services,” was published on International Journal of Geographical Information Science. The study demonstrated outperforming results, including reduced passenger rejection rates and driver idle time. With a focus on geospatial artificial intelligence (GeoAI), the RCAIG and the POLEIS at PolyU are dedicated to conducting research in diverse fields, including urban building and energy, urban safety and securing, environmental monitoring and conservation and urban resilience and public health. This aligns with the 11th United Nations Sustainable Development Goal (SDG11), which aims to create inclusive, safe, resilient, and sustainable cities and human settlements.
About RCAIG The research focus of RCAIG is on exploring and applying AI in geomatics. In particular, GeoAI represents the fusion of AI with geospatial data and technology, utilising a multidisciplinary approach to analyse, predict and visualise complex patterns within geospatial data. Its significance lies in its ability to provide profound insights and more accurate results compared to conventional geospatial techniques.
