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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.06057 |
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| _version_ | 1866909729401339904 |
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| author | Valipour, Mojtaba Zheng, Kelly Lowman, James Szabados, Spencer Gartner, Mike Braswell, Bobby |
| author_facet | Valipour, Mojtaba Zheng, Kelly Lowman, James Szabados, Spencer Gartner, Mike Braswell, Bobby |
| contents | Artificial General Intelligence (AGI) is closer than ever to becoming a reality, sparking widespread enthusiasm in the research community to collect and work with various modalities, including text, image, video, and audio. Despite recent efforts, satellite spectral imagery, as an additional modality, has yet to receive the attention it deserves. This area presents unique challenges, but also holds great promise in advancing the capabilities of AGI in understanding the natural world. In this paper, we argue why Earth Observation data is useful for an intelligent model, and then we review existing benchmarks and highlight their limitations in evaluating the generalization ability of foundation models in this domain. This paper emphasizes the need for a more comprehensive benchmark to evaluate earth observation models. To facilitate this, we propose a comprehensive set of tasks that a benchmark should encompass to effectively assess a model's ability to understand and interact with Earth observation data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06057 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AGI for the Earth, the path, possibilities and how to evaluate intelligence of models that work with Earth Observation Data? Valipour, Mojtaba Zheng, Kelly Lowman, James Szabados, Spencer Gartner, Mike Braswell, Bobby Computer Vision and Pattern Recognition Machine Learning Artificial General Intelligence (AGI) is closer than ever to becoming a reality, sparking widespread enthusiasm in the research community to collect and work with various modalities, including text, image, video, and audio. Despite recent efforts, satellite spectral imagery, as an additional modality, has yet to receive the attention it deserves. This area presents unique challenges, but also holds great promise in advancing the capabilities of AGI in understanding the natural world. In this paper, we argue why Earth Observation data is useful for an intelligent model, and then we review existing benchmarks and highlight their limitations in evaluating the generalization ability of foundation models in this domain. This paper emphasizes the need for a more comprehensive benchmark to evaluate earth observation models. To facilitate this, we propose a comprehensive set of tasks that a benchmark should encompass to effectively assess a model's ability to understand and interact with Earth observation data. |
| title | AGI for the Earth, the path, possibilities and how to evaluate intelligence of models that work with Earth Observation Data? |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2508.06057 |