Call for Papers: GeoAI Case Studies and Applications in the Environment
Special Collection in Case Studies in the Environment
Artificial intelligence (AI) is rapidly transforming geographic research and practice. Often referred to as GeoAI, the integration of AI methods with geographic information systems and science (GISc) enables new approaches to modeling complex environmental systems, detecting spatiotemporal patterns, and generating predictive insights across scales. In addition to novel analytical and modeling techniques, AI tools are increasingly embedded in existing geoprocessing and modeling workflows, enhancing efficiency, accessibility, and scalability.
Applications of GeoAI span a wide range of environmental domains, including climate change modeling, ecological forecasting, land cover classification and change detection, and real-time monitoring of natural hazards and resource use. As environmental datasets grow in volume and complexity, GeoAI offers powerful strategies for extracting actionable knowledge to inform policy and decision-making under uncertainty. At the same time, the expanding deployment of AI in environmental and geospatial contexts raises important questions related to data ownership and privacy, bias replication, governance, and the environmental impacts of widespread adoption.
This special collection invites submissions that present GeoAI-focused case studies centered on environmental problem-solving. We seek in-depth, real-world cases that examine how AI and geospatial data science are being applied in environmental contexts, with attention to both technical approaches and broader implications. Contributions may draw from research, teaching, practice, or public engagement, and should emphasize decision-making, tradeoffs, and lessons learned. Through these case studies, the collection will advance the integration of AI and geospatial technologies in environmental research, practice, and education.
Topics of interest include, but are not limited to:
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Environmental monitoring and modeling using AI tools
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AI-driven analysis of remote sensing or geospatial data applied to environmental challenges
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Integration of AI into decision-support systems for environmental planning or policy
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AI applications in climate, water, biodiversity, urban, or agricultural systems
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Case-based teaching or public engagement using AI in environmental contexts
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Ethical, equity, governance, or sustainability considerations in GeoAI applications
Case Studies in the Environment is a peer-reviewed journal published by University of California Press that features interdisciplinary case studies addressing contemporary environmental challenges and solutions. The journal also publishes articles that examine the pedagogy of using case studies for in-class instruction and other learning contexts. Submissions to this special collection should follow the Author Guidelines and emphasize specificity, interdisciplinarity, and transferable insights. In addition, submission to this special collection should include the procedure of AI tools and approaches applied for reproducibility. Authors should make use of the article narrative and/or teaching notes (i.e., supplemental information) to document the datasets used in the case and provide sufficient detail to support instructional implementation. Each case study will undergo a single-blind peer-review process, managed through the editorial interface Scholastica. After peer review, accepted articles will be added to the existing special collection on a rolling basis.
To be considered for this special issue, submit an extended abstract of 250-300 words to the special issue editors An-Min Wu (anminwu@usc.edu) and Jennifer Swift (jswift@usc.edu) by April 10, 2026. Your abstract should offer an overview of the case study as well as emphasize the specific AI or GeoAI approaches used for the environmental challenges and problem solving. Please direct additional questions to Editor-in-Chief Jennifer Bernstein (cse.editor@ucpress.edu) and/or Managing Editor Liba Hladik (lhladik@ucpress.edu).
Important Dates
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Abstract Submission: April 10, 2026
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Notification of Acceptance: May 1, 2026
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Full Article Submission: July 1, 2026
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Publication of Special Collection: Rolling basis