探索地球观测数据如何推动通用人工智能发展
AGI for the Earth, the path, possibilities and how to evaluate intelligence of models that work with Earth Observation Data?
- 提出地球观测数据对提升模型通用智能的关键作用
- 指出现有评估基准在泛化能力测试上的不足
- 建议构建涵盖多任务的综合性评估体系
通用人工智能（AGI）正迅速接近现实，激发了研究界对多模态数据（包括文本、图像、视频和音频）的广泛兴趣。尽管如此，卫星光谱影像作为重要模态仍未获得应有关注。该领域虽具独特挑战，却蕴含巨大潜力，可推动AGI对自然世界的理解能力。本文论证地球观测数据对智能模型的价值，并综述现有基准，揭示其在评估基础模型泛化能力方面的局限性。论文强调需建立更全面的评估基准，为此提出一套涵盖多任务的评估框架，以有效衡量模型对地球观测数据的理解与交互能力。
原文摘要 · Abstract (English)
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.
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