地球嵌入
Earth Embeddings
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中文总结 AI 辅助
该研究介绍地球嵌入的类型、特性与应用场景，通过案例展示其实用工作流程，为嵌入的选择等提供指导，并探讨相关开放问题，助力地球观测领域的高效分析。
中文摘要 AI 辅助
地球观测正从用户必须自行运行的基础模型，转向将模型特征输出打包为可重用数据的嵌入产品，无需下载和处理生成这些数据所用的影像。地球嵌入是汇总位置、图像块或像素的向量，让用户分析紧凑特征，而非在原始卫星影像上反复训练或运行大型模型。本章阐述地球嵌入的主要类型，从隐式位置编码器到显式图像块和像素产品，并比较它们的覆盖范围、分辨率、维度、存储成本、许可协议和可复现性。我们综述其在土地覆盖与作物制图、生态与灾害建模、社会经济预测及语义搜索中的应用，提供嵌入何时优于传统特征，以及池化、融合或空间迁移何时会限制性能的相关证据。两个案例研究展示相似性搜索和土地覆盖制图的实用工作流程。最后，我们给出选择、评估、存储、压缩和发布嵌入的指导，并探讨海洋与大气覆盖、不确定性及基准测试方面的开放问题。
英文摘要
Earth observation is moving from foundation models that users must run themselves toward embedding products that package model feature outputs as reusable data without needing to download and process the imagery used to generate them. Earth embeddings are vectors that summarize locations, image patches, or pixels, letting users analyze compact features instead of repeatedly training or running large models on raw satellite imagery. This chapter explains the main types of Earth embeddings, from implicit location encoders to explicit patch and pixel products, and compares their coverage, resolution, dimensionality, storage cost, licenses, and reproducibility. We review their use in land cover and crop mapping, ecological and hazard modeling, socioeconomic prediction, and semantic search, with evidence on when embeddings improve on conventional features and when pooling, fusion, or spatial transfer limit performance. Two case studies show practical workflows for similarity search and land cover mapping. We close with guidance for choosing, evaluating, storing, compressing, and publishing embeddings, and with open problems in oceanic and atmospheric coverage, uncertainty, and benchmarking.
