AI for Urban Climate: An Eo-Based Approach for High-Resolution Mapping of Human Exposure to Heatwaves

As climate change prospects emphasize the need for local adaptation, tackling heat exposure becomes a priority in Southern Europe. In the Greater Lisbon Area, heatwaves (HW) are already frequent, and their severity is expected to aggravate in the coming decades. Accordingly, there is a pressing need to pinpoint the neighborhoods where people are relatively more exposed to the excess heat. In this study, air temperature measurements from citizen owned stations are retrieved from open data sources, quality controlled and co-located with Earth Observation (EO) data to downscale the short-term numerical weather predictions (NWP) from 2.5km up to a metric scale (200m), using a Machine learning (ML) approach. While the NWP model resolves the regional physical processes, the ML model is able to disclose the air temperature response to more detailed local features, such as the urban heat island effect, supporting the local public health stakeholders in their decision-making.