为地球观测影像创建全局密集嵌入，构建全球最大开放地理视觉数据集。
Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space
- 扩展Major TOM项目，生成地球影像的全局密集嵌入表示。
- 发布4个全球覆盖的密集嵌入数据集，覆盖范围最广。
- 适合遥感、地理信息与跨模态学习研究者使用。
随着像哥白尼计划这类大型项目中地球观测数据量持续增长，对原始数据高效向量化表示的需求日益迫切。利用预训练深度神经网络提取特征表示是一种强大方法，可实现输入数据的语义抽象。然而，针对包含地理空间数据的影像档案，这一方法尚未形成标准。本文扩展了社区项目Major TOM，专注于提供和标准化开放免费的地球观测AI就绪数据集。同时，本文公开发布了4个全球且密集的嵌入数据集，使本研究成为迄今覆盖地球表面最全面的开放地理视觉嵌入数据集。
原文摘要 · Abstract (English)
With the ever-increasing volumes of the Earth observation data present in the archives of large programmes such as Copernicus, there is a growing need for efficient vector representations of the underlying raw data. The approach of extracting feature representations from pretrained deep neural networks is a powerful approach that can provide semantic abstractions of the input data. However, the way this is done for imagery archives containing geospatial data has not yet been defined. In this work, an extension is proposed to an existing community project, Major TOM, focused on the provision and standardization of open and free AI-ready datasets for Earth observation. Furthermore, four global and dense embedding datasets are released openly and for free along with the publication of this manuscript, resulting in the most comprehensive global open dataset of geospatial visual embeddings in terms of covered Earth's surface.
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