Leveraging urban AI for high-resolution urban heat mapping
Towards climate resilient cities
Bibliographic Data
| ID | 21246936 |
|---|---|
| Authors | Abdulrazzaq Shaamala (0000-0003-1683-4192, Queensland University of Technology), Niklas Tilly (0009-0008-0837-1144, Queensland University of Technology), Tan Yigitcanlar (0000-0001-7262-7118, Queensland University of Technology, corresponding author) |
| Year | 2025 |
| Volume | 52 |
| Issue | 9 |
| Pages | 2251-2266 |
| Publication date | 2025-11-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning B Urban Analytics and City Science (JOURNAL) |
| Journal identifiers | ISSN: 2399-8083 • E-ISSN: 2399-8091 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/23998083251337864 |
| OpenAlex | W4409766846 |
| Language | EN |
| Citations received | 1 |
| References cited | 67 |
Urban heat island (UHI) effects are increasingly recognised as a significant challenge arising from urbanisation, leading to elevated temperatures within urban areas that pose risks to public health and undermine the sustainability of cities. Effective UHI management requires high-resolution and timely mapping of urban temperature patterns to guide interventions. Traditional methods for urban heat mapping often lack the spatial accuracy and efficiency necessary for detailed analysis, especially in complex urban environments. This study integrates Urban artificial intelligence (Urban AI) by presenting a U-Net model tailored for urban heat mapping within the metropolitan area of Adelaide, South Australia. Trained on high-resolution thermal and spatial data from the South Australian Government Data Directory, the model captures pixel-level temperature variations across diverse urban landscapes, including densely built areas, suburban zones, and green spaces. Achieving a low Mean Squared Error (MSE) of 0.0029 and processing each map in less than 30 seconds, the model demonstrates exceptional accuracy and computational efficiency. The U-Net model, as an Urban AI agent, offers a scalable tool for urban heat analysis, supporting real-time assessments and facilitating targeted UHI mitigation efforts. By bridging the gap between advanced geospatial modelling and practical urban planning, it enables data-driven decisions that enhance climate resilience, optimise green infrastructure, and improve public health in rapidly urbanising regions. This approach highlights the transformative potential of Urban AI in addressing urban heat challenges, delivering precise and actionable insights to support sustainable and climate-adaptive urban environments
Civil engineering · Climate change · Geography · Meteorology · Remote sensing · Urban climate · Urban heat island · Urban planning · Engineering · Environmental Science · Noise Effects and Management · Urban Green Space and Health · Urban Heat Island Mitigation · Geology
The surface urban heat island response to urban expansion
Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes
Thermal remote sensing of urban climates
Climate change in cities due to global warming and urban effects
U-Net
Urban greening to cool towns and cities
Thermal infrared remote sensing for urban climate and environmental studies
Satellite Remote Sensing of Surface Urban Heat Islands
Developing an applied extreme heat vulnerability index utilizing socioeconomic and environmental data
Impact of urban form and design on mid-afternoon microclimate in Phoenix Local Climate Zones
The city and urban heat islands
Assessment with satellite data of the urban heat island effects in Asian mega cities
A review on the generation, determination and mitigation of Urban Heat Island
Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes
Effects of urban vegetation on microclimate and building energy demand in winter
Impacts of future urbanization on urban microclimate and thermal comfort over the Mumbai metropolitan region, India
Simulation and prediction of daytime surface urban heat island intensity under multiple scenarios via fully connected neural network
Artificial intelligence in local governments
Mapping Two Decades of Autonomous Vehicle Research
Investigating the urban heat island effect of transit oriented development in Brisbane
Artificial intelligence in local government services
Adapting cities for climate change through urban green infrastructure planning
Artificial intelligence and the local government
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |