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

Authors: YaHui Wang

Addresses: Electronic Information Department, Jinzhong Vocational and Technical College, Jinzhong, Shanxi, 030600, China

Abstract: 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.

Keywords: remote sensing; deep learning; environmental monitoring multi-module integration; spatial feature extraction.

DOI: 10.1504/IJDS.2025.149860

International Journal of Data Science, 2025 Vol.10 No.2, pp.156 - 174

Received: 25 Dec 2024
Accepted: 23 Apr 2025
Published online: 14 Nov 2025 *

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