Algorithmic Urban Greening for Thermal Resilience: AI-Optimised Tree Placement and Species Selection
Abdulrazzaq Shaamala; Tan Yigitcanlar; Alireza Nili; Dan Nyandega · 2025-08-13
This study presents an AI-driven framework that uses Ant Colony Optimisation, species-specific thermal traits, and high-resolution climate simulations to strategically place and select urban trees, achieving significant reductions in extreme heat and improved thermal comfort at the neighbourhood scale.
Abdulrazzaq Shaamala; Tan Yigitcanlar; Alireza Nili; Dan Nyandega. 2025-08-13. Algorithmic Urban Greening for Thermal Resilience: AI-Optimised Tree Placement and Species Selection. https://www.sciencedirect.com/science/article/pii/S0264275125006572 (AI & Environment Resource Hub; record paper-366; collection snapshot 2026-09-15).
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AI is already reshaping the way we live and work, but what role can it play in tackling the effects of climate change? This short, practical course analyses how we can harness the power of AI to drive climate solutions, from planning resilient cities to protecting nature and powering the energy transition. Along the way, you'll hear from Stanford experts, analyse global case studies and reflect on what this could mean in a public sector context.
Apolitical
Shared topics: Climate and greenhouse gases; Biodiversity and ecosystems; Weather, hazards, and adaptation; Built environment and infrastructure
This study uses random forest-based AI and Landsat imagery to assess how urban growth in Baghdad from 1985 to 2021 has significantly increased land surface temperatures, primarily due to reduced vegetation and moisture.
Asian Geographer
Shared topics: Climate and greenhouse gases; Biodiversity and ecosystems; Weather, hazards, and adaptation; Built environment and infrastructure
This study demonstrates that Random Forest-based spatial interpolation can accurately estimate urban PM2.5 levels in Brookline, MA, even with reduced sensor networks, offering a cost-effective approach to enhance air quality monitoring.
ICLR
Shared topics: Climate and greenhouse gases; Biodiversity and ecosystems; Weather, hazards, and adaptation; Built environment and infrastructure
This study demonstrates that machine learning models, particularly Random Forest Classifiers, can effectively simulate and classify precipitation and extreme weather patterns across North Indian states, offering valuable insights for disaster preparedness and water resource management.
