1642-2511 Emerging trends in GIS and remote sensing technologies for environmental monitoring: innovations, applications, and future directions Nowe trendy w technologiach GIS i teledetekcji w zakresie monitorowania środowiska: innowacje, zastosowania i przyszłe kierunki rozwoju Szafarczyk Anna author Agbasi Okechukwu Ebuka author Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie Michael Okpara University of Agriculture 050850526 Correspondence to: Anna Szafarczyk szafarcz@agh.edu.pl Correspondence to: Okechukwu Ebuka Agbasi agbasi.okechukwu@gmail.com 05 12 2025 Vol. 24 (2025) 2025 25 41 Copyright © 2025 2025 <p>The escalating challenges of climate change, biodiversity loss, land degradation, and urban expansion have amplified the need for reliable, high-resolution, and timely environmental data. Geographic Information Systems (GIS) and Remote Sensing (RS) technologies have become indispensable tools for environmental monitoring, enabling the systematic collection, analysis, and visualization of spatial data across diverse ecosystems. This review synthesizes recent innovations in GIS and RS that are transforming environmental surveillance and decision-making. Key developments include the integration of artificial intelligence (AI) and machine learning (ML) for enhanced image classification, cloud-based platforms like Google Earth Engine (GEE) for scalable analysis, and the increasing use of Unmanned Aerial Vehicles (UAVs) and hyperspectral sensors for high-resolution monitoring. Furthermore, the convergence of geospatial analytics with big data, the Internet of Things (IoT), and participatory approaches such as citizen science is expanding the accessibility and impact of environmental data. Case studies from Africa, Asia, and global initiatives highlight practical applications in land use change detection, water resource assessment, hazard risk mapping, urban heat island analysis, and biodiversity conservation. While the potential of these tools is vast, persistent challenges include data interoperability, technical capacity gaps, policy integration barriers, and ethical concerns related to surveillance and data equity. This review calls for greater investment in open-source tools, interdisciplinary collaboration, and inclusive data governance to realize the full potential of GIS and RS in achieving environmental resilience and sustainability. Future directions emphasise real-time monitoring, ethical frameworks, and the democratisation of spatial intelligence.</p> 1. Ibebuchi, C.C., Abu, I.O. Interpolation of environmental data using deep learning and model inference. 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