GeoAI technologies in earth observation to tackle environmental challenges
From heatwaves to pandemic diseases, the urban environments of the world face numerous challenges, 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.
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. Earth observation is important as a guiding compass for understanding changes in the environment and society.
Led by Prof. Qihao WENG, Chair Professor of Geomatics and Artificial Intelligence at the Department of Land Surveying and Geo-Informatics, the Research Centre for Artificial Intelligence in Geomatics (RCAIG) 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.
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.
The research conducted by Ms Wanru HE, a doctoral research assistant of the RCAIG and the team, titled “Modeling gridded urban fractional change using the temporal context information in the urban cellular automata model” was published on Cities. This model can well capture the dynamics of urban sprawl with significantly improved computational efficiency and performance, and it enables the modelling of urban growth at the regional even the globe, under diverse urbanisation scenarios in future.
