Synergizing Remote Sensing, Geospatial Intelligence, Applied Nonlinear Analysis, and AI for Sustainable Environmental Monitoring

Abstract

The incorporation of Remote Sensing, Geospatial Intelligence (GEOINT), and Artificial Intelligence (AI) for land cover classification facilitates the efficient gathering of data, sophisticated spatial analysis, and the development of prediction models. This collaborative method improves the precision and promptness of environmental monitoring, bolstering sustainable resource management and proactive decision-making.  The study used an advanced methodology involving a Modified VGG16 model, achieving an outstanding accuracy rate of 97.34%. This approach outperforms traditional algorithms, showcasing its efficacy in precisely classifying land cover categories. The utilization of remote sensing technology enables the effective gathering of data, while GEOINT enhances the spatial analysis capabilities using modern techniques. The AI-powered Modified VGG16 model has exceptional performance in predictive modeling, allowing for the implementation of proactive management measures. The abstract highlights the significant and revolutionary effects of this comprehensive method on environmental monitoring, providing unparalleled capacities for data analysis and decision-making. The findings highlight the importance of cooperation between researchers, policymakers, and industry stakeholders to fully utilize the capabilities of these technologies and tackle obstacles in sustainable environmental management.