Abdul Shaamala: Optimising green infrastructure for climate-resilient cities

Green infrastructure (GI) is critical in enhancing urban resilience, mitigating heat stress, and improving environmental sustainability. However, optimising the placement and configuration of green elements such as trees, parks, and vegetative corridors, requires a data-driven approach that accounts for microclimate variations, urban morphology, and long-term ecosystem benefits.

This talk explores how artificial intelligence (AI), machine learning (ML), and geospatial analysis can be leveraged to optimise GI for urban cooling and climate adaptation. Specifically, it delves into tree optimisation strategies that enhance shade provision, reduce urban heat islands (UHI), and improve outdoor thermal comfort. By utilising computational models, including optimisation algorithms and thermal analysis, cities can strategically position vegetation to maximise cooling benefits while balancing urban development needs.

Abdulrazzaq Shaamala is a PhD student specialising in urban planning, geospatial analysis, and AI-driven optimisation of green infrastructure. His research uses machine learning, computational modelling, and optimisation techniques to enhance urban cooling, mitigate urban heat islands, and improve climate resilience. 

He has developed AI-based methodologies for strategic tree placement and urban greening, integrating high-resolution thermal and spatial data to support climate-responsive city planning. His work aims to inform data-driven policies for greener, more resilient cities.