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Abstract GeoAI, or geospatial artificial intelligence (AI), has transformative potential for Earth science by integrating geospatial data with AI to enhance environmental monitoring, predictive modeling, and decision‐making. This commentary, based on the Greg Leptoukh Lecture at American Geophysical Union 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.
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Towards practical artificial intelligence in Earth sciences
Abstract Although Artificial Intelligence (AI) projects are common and desired by many institutions and research teams, there are still relatively few success stories of AI in practical use for the Earth science community. Many AI practitioners in Earth science are trapped in the prototyping stage and their results have not yet been adopted by users. Many scientists are still hesitating to use AI in their research routine. This paper aims to capture the landscape of AI-powered geospatial data sciences by discussing the current and upcoming needs of the Earth and environmental community, such as what practical AI should look like, how to realize practical AI based on the current technical and data restrictions, and the expected outcome of AI projects and their long-term benefits and problems. This paper also discusses unavoidable changes in the near future concerning AI, such as the fast evolution of AI foundation models and AI laws, and how the Earth and environmental community should adapt to these changes. This paper provides an important reference to the geospatial data science community to adjust their research road maps, find best practices, boost the FAIRness (Findable, Accessible, Interoperable, and Reusable) aspects of AI research, and reasonably allocate human and computational resources to increase the practicality and efficiency of Earth AI research.
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- PAR ID:
- 10539157
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Computational Geosciences
- Volume:
- 28
- Issue:
- 6
- ISSN:
- 1420-0597
- Format(s):
- Medium: X Size: p. 1305-1329
- Size(s):
- p. 1305-1329
- Sponsoring Org:
- National Science Foundation
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