Innovative AI–GIS Frameworks for Wetland Conservation: Technological Advances, Patent Trends, and Future Perspectives

Abstract

Background: Wetlands are among the world's most valuable ecosystems, providing biodiversity conservation, carbon sequestration, flood regulation, groundwater recharge, and water purification. However, they are increasingly threatened by urbanization, agricultural expansion, pollution, climate change, and hydrological disturbances. Recent advances in Artificial Intelligence (AI), Geographic Information Systems (GIS), remote sensing, and cloud-based technologies have significantly improved wetland monitoring and conservation. This review critically examines technological innovations, emerging patent trends, and future opportunities for AI–GIS-based wetland conservation. Methods: A narrative-systematic review was conducted following PRISMA guidelines. Literature published between 2000 and 2026 was retrieved from Scopus, Web of Science, PubMed, ScienceDirect, SpringerLink, IEEE Xplore, Google Scholar, and CrossRef. Patent information was collected from WIPO PATENTSCOPE, Google Patents, Espacenet (EPO), USPTO, and The Lens. Studies were screened using predefined inclusion criteria and synthesized to evaluate technological advances, patent trends, conservation applications, and future research directions.
Results: AI–GIS integration has significantly enhanced wetland mapping, biodiversity monitoring, habitat suitability assessment, hydrological modeling, pollution detection, and climate risk prediction. Machine learning and deep learning combined with satellite imagery, UAVs, and cloud platforms have improved environmental monitoring accuracy. Patent analysis indicates increasing innovation in AI-driven geospatial analytics, digital twins, IoT-based monitoring, and intelligent decision-support systems, although wetland-specific patent development remains limited.
Discussion: AI–GIS technologies are transforming wetland conservation through predictive and data-driven ecosystem management. However, challenges such as limited datasets, algorithmic bias, high computational costs, and inadequate field validation remain. Future research should focus on explainable AI, digital twins, real-time monitoring, and affordable, open-source technologies with greater commercialization potential.
Conclusion: Innovative AI–GIS frameworks offer sustainable solutions for wetland conservation by integrating intelligent monitoring with evidence-based management. Expanding patent activity highlights growing commercial interest, while future developments should prioritize scalable, cost-effective, and environmentally sustainable technologies to support long-term wetland resilience and conservation.