Remote sensing-based wireless spatial data analysis integrating machine learning and data fusion for enhanced environmental monitoring

Remote sensing is vital for environmental monitoring, aiding in land use classification, vegetation health assessment, and climate change analysis. This study introduces an integrated model combining convolutional neural networks (CNN), Random Forests (RF), and graph neural networks (GNNs) to improve remote sensing data classification. The model leverages spatial feature extraction, classification robustness, and spatial relationship capture for enhanced performance. Evaluated on MODIS and Sentinel-2 datasets, it achieved 95.18% and 90.88% accuracy, outperforming state-of-the-art methods in accuracy and efficiency. The model also demonstrated high recall, F1 scores, and computational efficiency, making it suitable for real-time and large-scale applications. Ablation studies confirmed the importance of each component, highlighting the model's potential for scalable and accurate environmental monitoring.