This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1029/2025JH000691. This is version 2 of this Preprint.
Artificial Intelligence in Earth Science: A GeoAI Perspective
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Abstract
GeoAI, or geospatial artificial intelligence, has transformative potential for Earth science by integrating geospatial data with artificial intelligence to enhance environmental monitoring, predictive modeling, and decision-making. This commentary, based on the Greg Leptoukh Lecture at AGU 2024, explores the evolving role of GeoAI in addressing pressing challenges—from environmental change in the Arctic to disaster response in hurricane-prone tropical regions. It highlights advancements in GeoAI-driven analysis of multimodal Earth observation data, ranging from structured remote sensing imagery to semi-structured data and natural language texts. The integration of knowledge graphs and generative AI further strengthens GeoAI by enabling seamless integration of cross-domain data, semantic reasoning, and knowledge inference. By bridging informatics and domain expertise, GeoAI is shaping a more intelligent and actionable digital future for Earth science.
DOI
https://doi.org/10.31223/X5M157
Subjects
Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Engineering, Environmental Sciences, Physical Sciences and Mathematics
Keywords
GeoAI, AI, reproducibility, Generative AI, Knowledge graph
Dates
Published: 2025-08-03 22:35
Last Updated: 2025-08-04 06:49
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License
CC-BY Attribution-NonCommercial 4.0 International
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Conflict of interest statement:
N/A
Data Availability (Reason not available):
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