Researchers at Hong Kong Polytechnic University are employing AI and informatics to address emerging concerns related to environmental changes and urban growth. 

The researchers are aiming to develop geospatial and AI technologies to offer solutions and insights into dynamic changes occurring in 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.

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. 

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, a 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 utilises machine learning and deep learning to 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. 

Professor Qihao Weng, Chair 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 AI methodologies and technologies for geomatics and their applications in urban areas.

Professor Weng  said, “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.