Deep Learning-Based Multisource Remote Sensing Data Fusion for Advanced Environmental Monitoring and Geospatial Intelligence Applications

Abstract

The rapid advancement in satellite technologies and sensor systems has enabled the acquisition of massive volumes of remote sensing data from multiple platforms such as optical, radar, LiDAR, and hyperspectral sensors. However, integrating these heterogeneous data sources to extract meaningful information remains a significant challenge. Deep learning has emerged as a powerful computational paradigm for data fusion in remote sensing, offering the ability to automatically learn hierarchical representations and fuse multisensor information efficiently. This paper explores the role of deep learning in remote sensing data fusion, covering key methodologies, recent developments, challenges, and potential applications in environmental monitoring and geospatial intelligence. The study also identifies future directions in multimodal deep fusion architectures, domain adaptation, and explainable artificial intelligence (XAI) for enhanced interpretability.

KEYWORDS: Deep learning, Remote sensing, Data fusion, Convolutional Neural Network (CNN), Hyperspectral imaging, Environmental monitoring, Geospatial intelligence, Multisensor integration.