Towards multi-modal oil spill detection and coverage in the Caspian Sea: a comprehensive approach

作     者：Pentayev, Alpamys Ahrari, Amirhossein Baubekova, Aziza Faizuldanov, Maksat Nurtayev, Nurzhan Sharifi, Alireza Haghighi, Ali Torabi Xenarios, Stefanos Fazli, Siamac 

作者机构：Nazarbayev Univ Dept Comp Sci Astana Kazakhstan Univ Oulu Water Energy & Environm Engn Res Unit Oulu Finland Nazarbayev Univ Grad Sch Publ Policy Astana Kazakhstan CSIRO Environm Canberra ACT Australia 

出 版 物：《INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT》 (Int. J. Water Resour. Dev.)

年 卷 期：2025年第41卷第1期

页      面：176-203页

核心收录：

学科分类：08[工学] 081501[工学-水文学及水资源] 0815[工学-水利工程] 

基　　金：Ministry of Education and Science of the Republic of Kazakhstan [AP19676581] Nazarbayev University, Competitive Research Grants Program [20122022FD4120] 

主　　题：Remote sensing oil spill detection deep learning image segmentation NLP 

摘      要：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.