Poster
in
Workshop: TerraBytes: Towards global datasets and models for Earth Observation
Towards LLM Agents for Earth Observation
Chia Hsiang Kao · Wenting Zhao · Shreelekha Revankar · Samuel Speas · Snehal M Bhagat · Rajeev Datta · Cheng Perng Phoo · Utkarsh Mall · Carl Vondrick · Kavita Bala · Bharath Hariharan
[
Abstract
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[ Project Page ]
presentation:
TerraBytes: Towards global datasets and models for Earth Observation
Sat 19 Jul 9 a.m. PDT — 5:30 p.m. PDT
[
OpenReview]
Sat 19 Jul 2 p.m. PDT
— 3 p.m. PDT
Sat 19 Jul 9 a.m. PDT — 5:30 p.m. PDT
Abstract:
Earth Observation (EO) provides critical planetary data for environmental monitoring, disaster management, climate science, and other scientific domains. Here we ask: Are AI agents ready for reliable Earth Observation? We introduce UnivEARTH, a benchmark of 140 yes/no questions from NASA Earth Observatory articles across 13 topics and 17 satellite sensors. Using Google Earth Engine API as a tool, LLM agents can only achieve an accuracy of 33% because the code fails to run over 58% of the time. Taken together, our findings identify significant challenges to be solved before AI agents can automate Earth observation and suggest paths forward.
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