A Distributed Data Management and Service Framework for Heterogeneous Remote Sensing Observations
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| Veröffentlicht in: | Remote Sensing vol. 17, no. 24 (2025), p. 4009-4032 |
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| 001 | 3286351819 | ||
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| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17244009 |2 doi | |
| 035 | |a 3286351819 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Cheng Hongquan |u School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou 510090, China | |
| 245 | 1 | |a A Distributed Data Management and Service Framework for Heterogeneous Remote Sensing Observations | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a <sec sec-type="highlights"> What are the main findings? <list list-type="bullet"> <list-item> </list-item>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. <list-item> 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. </list-item> What are the implications of the main findings? <list list-type="bullet"> <list-item> </list-item>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. <list-item> 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. </list-item> 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. | |
| 653 | |a Databases | ||
| 653 | |a Modularity | ||
| 653 | |a Environmental monitoring | ||
| 653 | |a Metadata | ||
| 653 | |a Data processing | ||
| 653 | |a Datasets | ||
| 653 | |a Data sources | ||
| 653 | |a Remote sensing | ||
| 653 | |a Image processing | ||
| 653 | |a Fault tolerance | ||
| 653 | |a Urban development | ||
| 653 | |a Efficiency | ||
| 653 | |a Distributed processing | ||
| 653 | |a Data management | ||
| 653 | |a Big Data | ||
| 653 | |a Spatial data | ||
| 653 | |a Infrastructure | ||
| 653 | |a Workload | ||
| 653 | |a Design | ||
| 653 | |a Modular design | ||
| 653 | |a Latency | ||
| 653 | |a Real time | ||
| 653 | |a Management | ||
| 653 | |a Relational data bases | ||
| 700 | 1 | |a Wu Huayi |u State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China | |
| 700 | 1 | |a Zheng, Jie |u Oriental Space Port Research Institute, Yantai 265100, China | |
| 700 | 1 | |a Li, Zhenqiang |u National Engineering Research Center for Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China | |
| 700 | 1 | |a Qi Kunlun |u National Engineering Research Center for Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China | |
| 700 | 1 | |a Gong Jianya |u State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China | |
| 700 | 1 | |a Longgang, Xiang |u State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China | |
| 700 | 1 | |a Cao Yipeng |u Oriental Space Port Research Institute, Yantai 265100, China | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 24 (2025), p. 4009-4032 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286351819/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286351819/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286351819/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |