Towards multi-modal oil spill detection and coverage in the Caspian Sea: a comprehensive approach
Pentayev A. Ahrari A. Baubekova A. Faizuldanov M. Nurtayev N. Sharifi A. Torabi Haghighi A. Xenarios S. Fazli S.
2025Routledge
International Journal of Water Resources Development
2025#41Issue 1176 - 203 pp.
This study presents a novel multi-modal methodology for detecting oil spills in the Caspian Sea and combines remote sensing, deep learning and natural language processing (NLP) of media content. We developed an accurate and comprehensive oil spill database covering incidents from 2002 to 2023 by integrating satellite synthetic aperture radar imagery with deep learning segmentation models. A key innovation of our approach is cross-referencing satellite-detected spills with media reports, enhancing detection accuracy while revealing significant underreporting of spills in media outlets. Our approach demonstrates the potential of merging technological innovations with media analytics to improve environmental monitoring effectiveness and inform policy-making for sustainable marine ecosystems.
deep learning , image segmentation , NLP , oil spill detection , Remote sensing
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Department of Computer Science, Nazarbayev University, Astana, Kazakhstan
Water, Energy and Environmental Engineering Research Unit, University of Oulu, Oulu, Finland
Graduate School of Public Policy, Nazarbayev University, Astana, Kazakhstan
CSIRO Environment, Canberra, ACT, Australia
Department of Computer Science
Water
Graduate School of Public Policy
CSIRO Environment
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Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026