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<p>The integration of artificial intelligence (AI) and geographic information systems (GIS) is transforming environmental and marine research by enabling advanced analytics, predictive modeling, and spatially informed decision-making. This review synthesizes 168 peer-reviewed studies to assess how AI-GIS integration enhances applications across terrestrial and aquatic domains, including climate modeling, biodiversity monitoring, pollution detection, ocean forecasting, and species tracking. Case studies from coral reef assessments to urban air-quality prediction demonstrate the operational value of these combined technologies. Persistent challenges such as data quality, computational demand, and limited model transparency remain barriers to widespread adoption. Future priorities emphasize open-access platforms, explainable and real-time AI systems, and cross-disciplinary training. By consolidating existing knowledge and identifying research frontiers, this review establishes a framework for leveraging AI-GIS integration toward sustainable environmental and marine management.</p> Graphical Abstract <p>The graphical abstract illustrates artificial intelligence (AI) and geographic information systems (GIS) integration for environmental and marine research. It shows data sources: satellites, in-situ sensors, and remote sensing feeding into interconnected AI-GIS integration systems that process and visualize environmental information. The lower panel highlights key applications, including climate adaptation, biodiversity monitoring, and marine spatial planning, demonstrating how AI-GIS integration supports sustainable resource management. The right panel outlines major challenges such as open-access data, explainable AI, and real-time analytics, emphasizing areas requiring further research. Using clean vector icons, a clear layout, and a blue-green palette, the figure conveys the flow from data collection to actionable insights, reflecting how AI-GIS integration enables faster, smarter, and more transparent environmental decision-making.</p>

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Artificial Intelligence and Geospatial Approaches Advancing Environmental and Marine Science

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Asif Raihan,
  • Syed Masiur Rahman,
  • Mahfuzur Rahman,
  • Md Masudur Rahman,
  • Syed Azher Hussain,
  • Alaeldeen Ibrahim Elhaj,
  • Mahmudur Rahman,
  • Bijoy Mitra

摘要

The integration of artificial intelligence (AI) and geographic information systems (GIS) is transforming environmental and marine research by enabling advanced analytics, predictive modeling, and spatially informed decision-making. This review synthesizes 168 peer-reviewed studies to assess how AI-GIS integration enhances applications across terrestrial and aquatic domains, including climate modeling, biodiversity monitoring, pollution detection, ocean forecasting, and species tracking. Case studies from coral reef assessments to urban air-quality prediction demonstrate the operational value of these combined technologies. Persistent challenges such as data quality, computational demand, and limited model transparency remain barriers to widespread adoption. Future priorities emphasize open-access platforms, explainable and real-time AI systems, and cross-disciplinary training. By consolidating existing knowledge and identifying research frontiers, this review establishes a framework for leveraging AI-GIS integration toward sustainable environmental and marine management.

Graphical Abstract

The graphical abstract illustrates artificial intelligence (AI) and geographic information systems (GIS) integration for environmental and marine research. It shows data sources: satellites, in-situ sensors, and remote sensing feeding into interconnected AI-GIS integration systems that process and visualize environmental information. The lower panel highlights key applications, including climate adaptation, biodiversity monitoring, and marine spatial planning, demonstrating how AI-GIS integration supports sustainable resource management. The right panel outlines major challenges such as open-access data, explainable AI, and real-time analytics, emphasizing areas requiring further research. Using clean vector icons, a clear layout, and a blue-green palette, the figure conveys the flow from data collection to actionable insights, reflecting how AI-GIS integration enables faster, smarter, and more transparent environmental decision-making.