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arXiv · 2311.10255

FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems

Shiyuan Luo·Juntong Ni·Shengyu Chen·Runlong Yu·Yiqun Xie·Licheng Liu·Zhenong Jin·Huaxiu Yao·Xiaowei Jia
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Abstract

Modeling environmental ecosystems is critical for the sustainability of our planet, but is extremely challenging due to the complex underlying processes driven by interactions amongst a large number of physical variables. As many variables are difficult to measure at large scales, existing works often utilize a combination of observable features and locally available measurements or modeled values as input to build models for a specific study region and time period. This raises a fundamental question in advancing the modeling of environmental ecosystems: how to build a general framework for modeling the complex relationships among diverse environmental variables over space and time? In this paper, we introduce a framework, FREE, that enables the use of varying features and available information to train a universal model. The core idea is to map available environmental data into a text space and then convert the traditional predictive modeling task in environmental science to a semantic recognition problem. Our evaluation on two societally important real-world applications, stream water temperature prediction and crop yield prediction, demonstrates the superiority of FREE over multiple baselines, even in data-sparse scenarios.

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Shiyuan Luo, Juntong Ni, Shengyu Chen, Runlong Yu, Yiqun Xie, Licheng Liu, Zhenong Jin, Huaxiu Yao, Xiaowei Jia. 2025-10-10. FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems. https://arxiv.org/abs/2311.10255

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