AI Driven IoT based Satellite remote sensing system: KSK Approach in Satellite Remote Sensing - Dr. Kazi Kutubuddin Sayyad Liyakat (International Journal of Satellite Remote Sensing, 2026)
The convergence of the Internet of Things (IoT) and satellite remote sensing has traditionally been bottlenecked by massive data latency and limited downlink bandwidth. This paper proposes a decentralized framework for an "AI-Driven IoT-based Satellite Remote Sensing System," which shifts the paradigm from raw data transmission to onboard edge-intelligence. By integrating lightweight convolutional neural networks (CNNs) directly into satellite payloads, the system performs real-time feature extraction and anomaly detection before transmission. We demonstrate that this architecture not only optimizes spectral data processing but also reduces telemetry overhead by filtering redundant environmental noise. Our findings suggest that deploying autonomous AI agents at the orbital edge is the critical lynchpin for achieving instantaneous global situational awareness in disaster management, precision agriculture, and maritime surveillance. his study proposes a high-performance satellite remote sensing system based on the KSK Approach (Knowledge-Sensors-Knowledge) for real-time land cover classification and environmental monitoring. The KSK approach optimizes satellite data processing by creating an intelligent loop of pre-defined domain knowledge, raw sensor data acquisition, and post-processed knowledge generation. Experimental results indicate that this framework significantly outperforms traditional unsupervised techniques, achieving an accuracy of (99.9%) and a recall of (97.9%) in classification tasks. By addressing the computational limitations of analyzing large-scale high-resolution imagery, the KSK approach enhances operational efficiency, providing robust and reliable data for disaster management and sustainable resource planning.
Keywords: AIIoT; KSK approach; Low latency Telemetry; remote sensing; satellite IoT