A Distributed Data Management and Service Framework for Heterogeneous Remote Sensing Observations

Abstract

What are the main findings?

We present DDMS, a distributed data management and service framework that consolidates heterogeneous remote sensing data sources, including optical imagery and InSAR point clouds, into a unified system for scalable and efficient management.

The framework introduces an integrated storage model combining distributed file systems, NoSQL, and relational databases, alongside a parallel computing model, enabling optimized performance for large-scale image processing and real-time data access.

What are the implications of the main findings?

DDMS significantly enhances the scalability and efficiency of remote sensing data management, providing a flexible solution for real-time service delivery in applications that require high-volume, diverse datasets such as disaster monitoring, environmental analysis, and urban development.

By incorporating elastic parallelism and modular design, DDMS supports dynamic, large-scale geospatial data processing, reducing latency, improving service responsiveness, and ensuring robust performance across varying workloads and data sizes.

Remote sensing imagery is a fundamental data source in spatial information science and is widely used in earth observation and geospatial applications. The explosive growth of such data poses significant challenges for online management and service, particularly in terms of storage scalability, processing efficiency, and real-time accessibility. To overcome these limitations, we propose DDMS, a distributed data management and service framework for heterogeneous remote sensing data that structures its functionality around three core components: storage, computing, and service. In this framework, a distributed integrated storage model is constructed by integrating file systems with database technologies to support heterogeneous data management, and a parallel computing model is designed to optimize large-scale image processing. To verify the effectiveness of the proposed framework, a prototype system was implemented and evaluated with experiments on representative datasets, covering both optical and InSAR images. Results show that DDMS can flexibly adapt to heterogeneous remote sensing data and storage backends while maintaining efficient data management and stable service performance. Stress tests further confirm its scalability and consistent responsiveness under varying workloads. DDMS provides a practical and extensible solution for large-scale online management and real-time service of remote sensing images. By enhancing modularity, scalability, and service responsiveness, the framework supports both research and practical applications that depend on massive earth observation data.