Integrating remote sensing and geospatial AI-enhanced ISAC models for advanced localization and environmental monitoring

Abstract

Remote sensing data is inherently complex, frequently consisting of substantial amounts of multi-dimensional data with time-series components and several spectral bands. Geographic information systems and image processing tools have historically handled the labor-intensive and computationally complex task of processing this data to extract usable information. Another major obstacle to interpretation may be the complexity of the data. This paper presents a novel approach to intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) systems, with a focus on precision agriculture applications. By leveraging IRS technology, the proposed method enhances both sensing and communication capabilities, providing reliable data collection and transfer in challenging rural environments. The study introduces a theoretical model and validates its performance through extensive simulations, focusing on achievable rate and localization accuracy. Recognizing the limitations of an ideal line-of-sight channel assumption, we propose incorporating more complex channel models to account for real-world multipath effects. Additionally, we expand the evaluation metrics to include energy consumption, computational complexity, and latency, essential for practical applications. Our comparative analysis with advanced IRS-assisted ISAC schemes demonstrates the system’s robustness and efficiency. To further substantiate our findings, we include a small-scale prototype system test, offering empirical data that strengthens the theoretical insights and simulations. This multi-dimensional evaluation confirms the system’s suitability for deployment in real-world precision agriculture.