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To address these issues, this study introduces a Semantic Web-enabled framework combined with superpixel-based image segmentation for effective environmental monitoring. Leveraging aerial imagery and artificial intelligence to monitor these effects, employing a Simple Linear Iterative Clustering (SLIC) algorithm for image segmentation and a combination of classifiers (Support Vector Machine, SVM; Random Forest, RF; and Naive Bayes, NB) for land change detection. The effectiveness of this Semantic Web-enabled approach is demonstrated by its ability to accurately identify areas affected by ASGM, with robust statistical metrics including a kappa coefficient of 0.7616, an F1 score of 0.8806, and a Jaccard index of 0.798. 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