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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.11183 |
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| _version_ | 1866914258807160832 |
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| author | Chen, Shuang Wang, Jie Yuan, Shuai Li, Jiayang Xia, Yu Liao, Yuanhong Wei, Junbo Yuan, Jincheng Xu, Xiaoqing Zhu, Xiaolin Zhu, Peng Zhang, Hongsheng Zhou, Yuyu Fu, Haohuan Huang, Huabing Chen, Bin Dai, Fan Gong, Peng |
| author_facet | Chen, Shuang Wang, Jie Yuan, Shuai Li, Jiayang Xia, Yu Liao, Yuanhong Wei, Junbo Yuan, Jincheng Xu, Xiaoqing Zhu, Xiaolin Zhu, Peng Zhang, Hongsheng Zhou, Yuyu Fu, Haohuan Huang, Huabing Chen, Bin Dai, Fan Gong, Peng |
| contents | 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. This compression allows the entire global land surface for a single year to be encapsulated within approximately 2.4 TB, enabling decadal-scale global analysis on standard local workstations. Rigorous validation demonstrates high reconstructive fidelity (MAE: 0.0130; RMSE: 0.0179; CC: 0.8543). By condensing the annual phenological cycle into 12 temporal steps, the embeddings provide inherent denoising and a semantically organized space that outperforms raw reflectance in land-cover classification, achieving 79.74% accuracy (vs. 76.92% for raw fusion). With robust few-shot learning capabilities and longitudinal consistency, ESD provides a versatile foundation for democratizing planetary-scale research and advancing next-generation geospatial artificial intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11183 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Democratizing planetary-scale analysis: An ultra-lightweight Earth embedding database for accurate and flexible global land monitoring Chen, Shuang Wang, Jie Yuan, Shuai Li, Jiayang Xia, Yu Liao, Yuanhong Wei, Junbo Yuan, Jincheng Xu, Xiaoqing Zhu, Xiaolin Zhu, Peng Zhang, Hongsheng Zhou, Yuyu Fu, Haohuan Huang, Huabing Chen, Bin Dai, Fan Gong, Peng Computer Vision and Pattern Recognition 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. This compression allows the entire global land surface for a single year to be encapsulated within approximately 2.4 TB, enabling decadal-scale global analysis on standard local workstations. Rigorous validation demonstrates high reconstructive fidelity (MAE: 0.0130; RMSE: 0.0179; CC: 0.8543). By condensing the annual phenological cycle into 12 temporal steps, the embeddings provide inherent denoising and a semantically organized space that outperforms raw reflectance in land-cover classification, achieving 79.74% accuracy (vs. 76.92% for raw fusion). With robust few-shot learning capabilities and longitudinal consistency, ESD provides a versatile foundation for democratizing planetary-scale research and advancing next-generation geospatial artificial intelligence. |
| title | Democratizing planetary-scale analysis: An ultra-lightweight Earth embedding database for accurate and flexible global land monitoring |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.11183 |