CASE STUDY
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
In the field of urbanisation monitoring , an RCAIG research team has developed an impervious surface area 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 .
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 , this innovation solves the complex planning issues in transportation and offers a new perspective on a longterm spatio-temporal planning problem .
The research conducted by Mingyue Xu , another RCAIG researcher , and her team , was reported in the paper Multi-agent reinforcement learning to unify order-matching and vehicle-repositioning in ride-hailing services and has been published in the International Journal of Geographical Information Science .
The study achieved outstanding results , including reduced passenger rejection rates and driver idle time .
By categorising the historical pathways of urban area growth into 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 Wanru He , an RCAIG doctoral research assistant , and her team , Modeling gridded urban fractional change using the temporal context information in the urban cellular automata model has been published in Cities .
Their model effectively captures the dynamics of urban sprawl with significantly improved computational efficiency and performance and will help enable the modelling of urban growth at regional and even global level , under diverse future urbanisation scenarios .
With a focus on GeoAI , RCAIG is dedicated to conducting research in diverse fields , including urban building and energy , urban safety and security , environmental monitoring and conservation , urban resilience and public health .
This aligns with the 11th United Nations Sustainable Development Goal , which aims to create inclusive , safe , resilient and sustainable cities and human settlements .
RCAIG is a collaborative centre comprising five academic departments : the Department of Land Surveying & Geo-Informatics ( LSGI ), the Department of Building and Real Estate ( BRE ), the Department of Civil and Environmental Engineering ( CEE ), the Department of Computing ( COMP ), and the Department of Applied Mathematics ( AMA ), which belong to three faculties : the Faculty of Construction and Environment ( FCE ), the Faculty of Engineering ( FENG ) and the Faculty of Science ( FS ). p
GeoAI for traffic management
In the area of smart traffic management , to enhance the efficiency of ride-hailing platforms and achieve intelligent management of their services , the RCAIG research team has developed a multi-agent order matching and vehicle repositioning approach .
This innovative technology focuses on coordinating the supply and demand of ride-hailing services , ultimately aiming to improve their overall efficiency .
Their approach provides a ground-breaking solution to tackle two critical aspects necessary for efficient ridehailing services .
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