Semantic Web-Enabled Superpixel-Enhanced Environmental Monitoring for Land Use in ASGM Areas

Abstract

Addressing these challenges requires innovative monitoring techniques that can accurately assess the impact on land use and land cover. 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. These results underscore the method's capability in providing detailed insights into land cover changes, thereby serving as a significant tool for environmental monitoring and aiding in policy formulation.