GeoAI in Environmental Change Detection Using Satellite Imagery: Towards Smart Spatial Planning
DOI:
https://doi.org/10.5281/zenodo.21995223Keywords:
GeoAI, Remote Sensing, Deep Learning, Satellite Imagery, Environmental Monitoring, Smart Spatial PlanningAbstract
This study examines the application of Geospatial Artificial Intelligence (GeoAI) for monitoring environmental changes using satellite imagery as a core input for smart spatial analysis. A systematic literature review was conducted on thirty peer-reviewed studies published between 2019 and 2025, addressing urban expansion, desertification, climate change, flooding, and vegetation degradation.
Findings reveal that Deep Learning models—particularly Convolutional Neural Networks (CNNs) and Random Forests (RF)—are the most frequently employed, improving environmental change detection accuracy by 10–25% compared to conventional methods. Open-source platforms such as Google Earth Engine and Python have also proven highly effective for large-scale spatial processing.
The review highlights limited research in Arab regions and weak integration of environmental and socio-economic datasets. A conceptual framework is proposed to leverage GeoAI for data-driven decision-making toward sustainable environmental management and smart spatial planning.
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