Exploring the potential of AlphaEarth and TESSERA embeddings for Fine-scale Local Climate Zone Mapping: A case study across five cities in Switzerland
arXiv:2606.20034v1 Announce Type: new Abstract: Understanding urban spatial morphology is critical for climate modeling, risk assessment, and sustainable urban design, and Local Climate Zone (LCZ) mapping provides the basic framework for this. However, many cities still use coarse ~100-m resolution LCZ records, which are unsuitable for fine-scale urban research. In this study, precomputed embeddings from TESSERA (Feng et al., 2025) and AlphaEarth (Brown et al., 2025) are compared to traditional Sentinel-1/2 (S1S2) composites in five Swiss cities to see if they can upscale coarse LCZ maps to 10
The continuous development and availability of advanced AI models like AlphaEarth and TESSERA, coupled with increasing computational power, are enabling finer-grained environmental analysis.
Improved fine-scale climate zone mapping is crucial for precise urban planning, climate resilience, and resource management, directly impacting policy and infrastructure decisions.
The accuracy and resolution of urban climate zone maps can significantly improve, moving beyond coarse traditional methods and enabling more granular interventions.
- · Urban planners
- · Climate researchers
- · AI model developers
- · Smart city initiatives
- · Developers of legacy climate mapping software
- · Cities relying on outdated data
More accurate urban climate models facilitate better climate change adaptation strategies.
Enhanced resilience against extreme weather events and heat islands improves public health and urban sustainability.
The application of advanced AI to environmental mapping could become a standard, fostering further innovation in Earth observation and spatial intelligence.
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Read at arXiv cs.LG