Democratizing planetary-scale analysis: An ultra-lightweight Earth embedding database for accurate and flexible global land monitoring
Shuang Chen, Jie Wang, Shuai Yuan, Jiayang Li, Yu Xia, Yuanhong Liao, Junbo Wei, Jincheng Yuan, Xiaoqing Xu, Xiaolin Zhu, Peng Zhu, Hongsheng Zhang, Yuyu Zhou, Haohuan Fu, Huabing Huang, Bin Chen, Fan Dai, Peng Gong

TL;DR
This paper introduces ESD, an ultra-lightweight global Earth embedding database that significantly reduces data volume, enabling efficient, accurate, and flexible planetary land monitoring and analysis on standard hardware.
Contribution
The authors present ESD, a novel, highly compressed Earth embedding database that transforms multi-sensor satellite data into a unified, low-dimensional latent space for scalable global land analysis.
Findings
340-fold data volume reduction compared to raw archives
High fidelity in data reconstruction (MAE: 0.0130)
Improved land-cover classification accuracy (79.74%)
Abstract
The rapid evolution of satellite-borne Earth Observation (EO) systems has revolutionized terrestrial monitoring, yielding petabyte-scale archives. However, the immense computational and storage requirements for global-scale analysis often preclude widespread use, hindering planetary-scale studies. To address these barriers, we present Embedded Seamless Data (ESD), an ultra-lightweight, 30-m global Earth embedding database spanning the 25-year period from 2000 to 2024. By transforming high-dimensional, multi-sensor observations from the Landsat series (5, 7, 8, and 9) and MODIS Terra into information-dense, quantized latent vectors, ESD distills essential geophysical and semantic features into a unified latent space. Utilizing the ESDNet architecture and Finite Scalar Quantization (FSQ), the dataset achieves a transformative ~340-fold reduction in data volume compared to raw archives.…
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Taxonomy
TopicsRemote Sensing in Agriculture · Remote-Sensing Image Classification · Geographic Information Systems Studies
