Offering Expertise in Geospatial AI for Urban Sustainability and Resilience for MSCA project collaboration and proposal development

My ongoing projects focus on leveraging or developing Geospatial AI or Digital Twins to facilitate urban sustainability and resilience.

1. AI Meets Glasgow’s Trees: Metrics Prediction, 3D Mapping, and Socio-Ecosystem Impact Simulations. 

The project aims to harness the power of AI to advance sustainable forestry and woodland management in Glasgow, focusing on predicting tree metrics (e.g., species, height, canopy area, and tree health), creating a detailed and interactive 3D tree map, and assessing tree impacts on socio-ecosystem. This research will combine cutting-edge deep learning techniques and statistical inference with diverse data sources, including high-resolution LiDAR data, remote sensing imagery, street view images, environmental sensor data, citizen-science text, and social media. By leveraging exascale GPU computing, the project will develop scalable, real-time models and tools to support sustainable urban planning, biodiversity conservation, and climate resilience efforts.

The project will comprise two steps: (1) AI-powered predicting tree metrics, and developing 3D visualisation of Glasgow’s forestry and woodland; and (2) socio-ecosystem simulations to evaluate trees’ impacts on biodiversity, climate mitigation, and human well-being. These components will enable researchers, city planners, and policymakers to make data-driven decisions for a greener, more sustainable Glasgow.

2. Transit Resilience and Decarbonisation under Extreme Weather

The project aims to harness the power of large language models (LLMs) to advance sustainable and resilient transport networks under extreme weather. Over decades, the escalating impacts of climate change have increased extreme weather events - heatwaves, floods, storms - that challenge urban transportation systems globally by disrupting infrastructure and altering travel patterns. Prolonged extreme heat and frequent disasters discourage low-carbon travel modes like walking and cycling, pushing people toward higher-carbon options like private cars and taxis. This shift exacerbates traffic congestion and emissions, creating a feedback loop that accelerates climate change.

Despite growing climate awareness, a significant gap exists between understanding global implications and recognising direct impacts on daily life, particularly individual travel choices. To this end, advanced technologies such as LLMs can bridge this gap by transforming complex climate data into accessible, personalised information that motivates sustainable behavioral change. By providing real-time feedback on travel choices and suggesting sustainable alternatives, LLM-based tools encourage low-carbon decisions. Moreover, data collected from user interactions with these tools can inform and refine road network planning, creating a feedback loop where user behavior directly shapes infrastructure optimisation.

Please feel free to reach out if you are interested in a further discussion about project collaboration and/or proposal ideas.