PICO
Text-as-data, emerging data sources, and Large Language Models: Transforming Discovery in Geo- and Earth System Sciences
AGU
Convener:
Lina SteinECSECS
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Co-conveners:
Jens Klump,
Mariana Madruga de BritoECSECS,
Ni LiECSECS,
Minghua Zhang,
Georgia Destouni,
Gabriele Messori
At the same time, large language models (LLMs) are revolutionising the field by enabling researchers to process and interpret complex geological, climatological, environmental, hydrological, and other earth systems data with unprecedented speed and accuracy, leading to new discoveries and insights.
The session scope spans data analysis methodologies, scientific advances from the analysis of emerging data, and broader perspectives on the opportunities and challenges that these data sources present. Specific topics include but are not limited to, for example: assessment of natural hazard impacts (e.g. floods, droughts, landslides, temperature extremes, windstorms), real-time monitoring of disasters, evidence synthesis, public sentiment analysis, policy and awareness tracking, discourse and narrative analyses, natural language processing, large language models, social media analysis, historical data rescue, image mining, deep learning, and machine learning.
This session will provide a platform for geoscientists to discuss the integration of LLMs and novel data types into their workflows, enhancing both efficiency and discovery while addressing challenges such as model accuracy and data bias. We invite presentations that explore the transformative potential of large language models and text data in the geosciences. Join us in contributing to this cutting-edge dialogue and helping shape the future of geosciences through AI.