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arXiv cs.AI· Yuchi Ma, Jiaqi Yang, Mengyu Liang, Ivan Higuera-Mendieta, Caleb Robinson, Anthony Ortiz, Min Chen, David B. Lobell, Inbal Becker-Reshef, Juan M. Lavista Ferres·· 2 小时前AI 评分32

Earth Embeddings 在环境遥感中的应用综述

Earth Embeddings in Environmental Remote Sensing

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一项研究系统综述了 Earth embeddings——由多源卫星地球观测转换的通用表征,可用于下游任务且无需任务特定特征设计。研究回顾了代表性 Earth embedding 产品,覆盖环境遥感六大领域,并在高光谱图像分类、单木树冠分割、作物产量预测和地上生物量制图四项任务中评估性能。结果表明其并不总优于遥感影像或手工特征,宜视为跨时空稳定信息的表征而非传统观测数据的直接替代。

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Abstract:Earth observations from satellite platforms have profoundly transformed research and applications across numerous domains. However, uneven observation coverage and frequent data gaps continue to limit their ability to capture Earth's surface dynamics. Moreover, processing multi-modal Earth observations requires substantial computational resources and sophisticated data harmonization frameworks, which may not be readily accessible to many remote sensing practitioners. Earth embeddings have emerged as promising solutions to these challenges. As general-purpose representations converted from multi-source Earth observations, Earth embeddings can support a wide range of downstream tasks with no requirement of task-specific feature design and preprocessing. In this study, we first introduce the theoretical foundations of Earth embeddings. Then, we review several representative Earth embedding products. We next examine the applications of Earth embeddings across six major domains of environmental remote sensing. Finally, we systematically assess the performance of Earth embeddings in four downstream tasks, including hyperspectral image classification, individual tree crown segmentation, crop yield prediction, and aboveground biomass mapping. Our review and case studies suggest that, despite several advantages over conventional Earth observation data, current Earth embeddings do not always outperform remote sensing imagery or hand-crafted features. Earth embeddings should not be viewed as a direct replacement for conventional EO data, but rather as representations that provide stable information across space and time. Remote sensing practitioners may consider evaluating Earth embeddings' suitability for specific tasks and using them strategically, rather than assuming superior performance by default.
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2611.13612 [cs.AI]
  (or arXiv:2611.13612v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2611.13612

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yuchi Ma [view email]
[v1] Tue, 10 Nov 2026 05:42:10 UTC (9,432 KB)

来源:arXiv cs.AI · arxiv.org