arXiv:2607.03298v1 Announce Type: cross Abstract: Foundation models for Earth systems have so far been trained primarily on physical climate and weather data, with limited representation of the human systems that both drive and respond to environmental change. The lack of a unified global training resource that combines climate, land, ocean, cryosphere, infrastructure, hazards, and socioeconomic data on a common grid hinders progress toward truly multimodal Earth system foundation models. We present WorldTensor, a harmonised global dataset that aligns hundreds of environmental and socioeconomi
The increased drive for Earth system foundation models necessitates comprehensive, harmonized datasets, which were previously underdeveloped, making this dataset a timely and critical enabler.
This new dataset addresses a significant limitation in developing sophisticated, multimodal Earth system foundation models by integrating diverse environmental and socioeconomic data, crucial for understanding complex global challenges.
The availability of WorldTensor enables the training of more holistic and accurate AI models that can better predict and analyze the interconnected dynamics of climate, human systems, and infrastructure.
- · Climate scientists
- · AI model developers
- · Environmental research institutions
- · Governments planning for climate change
- · Developers relying solely on siloed data
- · Regions unprepared for environmental shifts
The harmonized dataset significantly accelerates the development and accuracy of Earth system foundation models.
Improved models lead to more precise climate predictions and better-informed policy decisions regarding environmental adaptation and mitigation.
Enhanced understanding of Earth systems' interplay may uncover novel solutions for resource management and disaster preparedness, potentially mitigating future environmental crises.
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