Enhanced Remote Sensing Monitoring Through a Bimodal Cloud Infrastructure: A Dual-State Cloud Service Approach

Abstract

This study addresses significant challenges within the field of remote sensing monitoring, including operational inefficiency, data confidentiality concerns, high hardware costs, and issues with data management and distribution. To tackle these problems, we introduce a synergistic remote sensing monitoring framework that leverages a bimodal cloud infrastructure, facilitated by cloud services to provide on-demand resource allocation and efficient management. Our research focuses on designing and developing an integrated operating system that optimizes remote sensing monitoring processes and enhances operational efficiency through the use of a mutual scheduling mechanism and rapid data indexing capabilities. The system is underpinned by a dual-state cloud service mechanism, combining the Memory Cloud (Flash Cloud) known for its high-speed data processing and the Storage Cloud (Persistent Cloud) for long-term data retention. This dual-state approach establishes a multi-level caching system to ensure quick access to frequently requested spatial data. Additionally, a two-tier security system is implemented to safeguard data integrity and confidentiality. Our “YunYao” geographic information service rendering engine, operating on this dual-state cloud platform, demonstrated remarkable performance advantages over mainstream platforms in identical testing environments. Specifically, it outperformed ArcGIS Desktop by over two times, exceeded GeoServer by more than four times, and was over seven times faster than ArcGIS Server in rendering speeds. Experimental and practical applications have shown that our system streamlines routine workflows and enhances work efficiency, making it a critical reference for remote sensing monitoring. Furthermore, a comparative analysis was conducted to quantitatively demonstrate the superior performance of our method in handling large volumes of remote sensing data(Including satellite imagery and UAV imagery). Despite these advancements, the integration of cloud service technology in the field of satellite remote sensing requires further development, particularly regarding the establishment of private clouds and the internal collaborative computing mechanisms within the remote sensing domain. Our research paves the way for future advancements and the eventual full integration of cloud service models into remote sensing monitoring.

Keywords

• Cloud service
• remote sensing monitoring
• bimodal cloud
• load balancing