KLIMASCANNER: An AI-Powered QGIS Plugin for Climate-Resilient Urban Planning

As climate change intensifies, ensuring thermal comfort in urban environments becomes a crucial challenge for public health and well-being. Urban planning plays a pivotal role in mitigating the effects of climate change by integrating climate-sensitive design strategies such as tree planting and facade greening. However, effective implementation requires an interdisciplinary understanding of the built environment, involving expertise from urban planning, ecology, and climatology. Additionally, city-based climate services face barriers such as limited data accessibility, communication challenges between stakeholders, and the lack of integrated, user-friendly tools.

Microscale RANS (Reynolds Averaged Navier-Stokes) models offer high-resolution urban climate simulations (up to 5 m spatial resolution), incorporating complex interactions between terrain, buildings, land use, and vegetation. However, their computational intensity often makes them impractical for routine planning applications. Simulating a city’s baseline climate state alone can take weeks on commercially available servers, while additional assessments of climate adaptation measures or new urban developments further increase computational demands. Although high-performance computing resources are available in research institutions, their access and costs remain prohibitive for many urban stakeholders.

To overcome these limitations, we developed KLIMASCANNER, an AI-powered QGIS plugin that integrates a neural network trained on RANS simulations to predict urban climate parameters such as air temperature (day and night), wind speed, and cold air flow for an autochthonous summer radiation day. By significantly reducing computational time while maintaining a high level of accuracy, the tool enables rapid assessments of urban development impacts on the local climate. KLIMASCANNER is designed to be accessible to urban planners, architects, and municipal decision-makers without requiring expertise in climate modeling. This facilitates informed decision-making and fosters climate-resilient urban design, bridging the gap between urban planning and climate science.