用卫星嵌入向量实现10米级城市气候区精细制图，省去繁琐预处理。
Exploring the potential of AlphaEarth and TESSERA embeddings for Fine-scale Local Climate Zone Mapping: A case study across five cities in Switzerland
- 用AlphaEarth和TESSERA嵌入替代传统遥感数据，输入注意力U-Net模型
- 在5个瑞士城市测试，交并比达0.59–0.82，优于传统方法
- 嵌入向量提升跨区域迁移能力，适合全球城市气候研究应用
理解城市空间形态对气候建模、风险评估和可持续城市设计至关重要，局部气候区（LCZ）制图为此提供基础框架。然而，许多城市仍使用约100米分辨率的粗略LCZ记录，难以支持细粒度城市研究。本研究在瑞士五个城市中，比较TESSERA（Feng等，2025）和AlphaEarth（Brown等，2025）的预计算嵌入向量与传统哨兵1/2（S1S2）复合数据，评估其能否通过基于注意力的U-Net将粗略LCZ图升至10米分辨率。三个实验分别检验多城市可迁移性、高分辨率参考数据的影响及年际物候变化下的时间鲁棒性。结果表明，所有数据集表现良好，测试交并比（IoU）在0.59–0.69和0.77–0.82之间；TESSERA在两种设置下均持续优于S1S2和AlphaEarth。如预期，嵌入模型在跨年度迁移方面仍存在挑战。总体而言，结果表明来自地球观测基础模型的嵌入具有巨大潜力，可减少耗时的预处理和人工特征工程，推动通用深度学习驱动的LCZ制图流程。结合简单的位置感知注意力U-Net架构，嵌入增强了区域迁移性和可扩展性，支持全球城市气候应用中的全面、可复现的细粒度LCZ地图开发。提升参考数据质量仍是进一步提高精度的最强杠杆。
原文摘要 · Abstract (English)
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-m resolution using an attention-based U-Net. Three experiments assess multi-city transferability, the impact of higher-resolution reference data, and temporal robustness to year-to-year phenology changes. We find that all datasets achieve strong performance with test data Intersection-over-Union (IoU) ranging from 0.59-0.69 and 0.77-0.82 in the first two experiments. TESSERA consistently outperforms both S1S2 and AlphaEarth across both settings As expected, we find that the transfer of embedding-based models from one year to another remains an open challenge. Overall, however, our results demonstrate the promising potential of embeddings derived from EO foundation models to reduce time consuming preprocessing, respectively, manual feature engineering tasks and to guide a universal deep learning-based LCZ mapping workflow. When combined with a simple location-aware attention U-Net architecture, the embeddings enhance regional transferability and scalability, supporting the development of comprehensive and reproducible fine-scale LCZ maps for global urban climate applications Improving reference data quality remains the strongest lever for further accuracy gains.
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