Problem
Cities need fine-grained LST maps to evaluate cooling interventions, but Landsat thermal data is too coarse (100m) for street-level analysis. No open high-resolution thermal baseline exists for Berlin.
Solution
Deep learning model downscales Landsat LST to Sentinel-2 resolution (10m) using spectral and structural features. Causal analysis links local adaptation measures (trees, sealed surfaces, water bodies) to temperature differences across the city.
Result
In progress. Target: reproducible Berlin UHI map at 10m resolution + quantified effect estimates per adaptation category.
Lessons Learned
- To be filled after completion.
Deep Dive
Data stack: Landsat 8/9 thermal band, Sentinel-2 optical bands, Geoportal Berlin (ISU5), DWD climate stations. Infrastructure: GCP (Vertex AI, BigQuery), Google Earth Engine for preprocessing.