A Discrete Grid-Based Approach for Efficient Near-Optimal Coverage Selection in a Large-Scale Remote Sensing Image Dataset.
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| Title: | A Discrete Grid-Based Approach for Efficient Near-Optimal Coverage Selection in a Large-Scale Remote Sensing Image Dataset. |
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| Authors: | Wang, Han1 (AUTHOR), Jiang, Haiyang2 (AUTHOR), Jiang, Yangming3 (AUTHOR), Wang, Yuchen4 (AUTHOR), Zhao, Jing5 (AUTHOR), Li, Liping1,3 (AUTHOR), Huang, Wenjiang2,3 (AUTHOR), Wang, Tuo3 (AUTHOR) wangtuo@aircas.ac.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1855. 20p. |
| Subjects: | Grids (Cartography), Discretization methods, Electronic data processing, Scalability, Remote sensing, Optimization algorithms, Spatial data structures |
| Abstract: | Highlights: What are the main findings? A DGGS-based method converts vector topology into grid-encoded set operations, reducing computational complexity for large-scale coverage selection. Results closely match those of vector methods, achieving ~100% coverage; efficiency improves by up to 11×, with level 7 grids offering the best trade-off. What are the implications of the main findings? DGGS enables a scalable spatial framework for efficient large-scale remote sensing coverage retrieval and optimization. Robust performance is maintained under sparse and partial coverage, supporting PB-scale data management and rapid preprocessing. Regional image coverage retrieval is a critical problem in large-scale remote sensing data processing. However, traditional vector topology-based methods suffer from rapidly increasing computational costs when handling massive and highly overlapping datasets. This paper proposes a coverage retrieval approach based on the Discrete Global Grid System (DGGS), which transforms geometric topological operations into grid-encoding set operations, thereby reconstructing the coverage computation process under a discrete spatial indexing framework. A heuristic greedy strategy is integrated to achieve efficient coverage selection. Experimental results demonstrate that the proposed DGGS-based method achieves speedups ranging from approximately 1.5× to 11×, depending on dataset scale and coverage. Grid-level analysis indicates that level 7 grids generally provide a favorable balance between spatial accuracy and computational efficiency for near-complete coverage retrieval, whereas level 6 grids offer a more computationally efficient alternative for rapid coverage estimation and sparse-coverage scenarios, with only marginal accuracy loss. Furthermore, the method exhibits near-linear scalability with increasing data volume and maintains stable performance under incomplete coverage scenarios. The results confirm that DGGS-based discrete modeling significantly reduces computational complexity while preserving spatial reliability, providing an efficient and scalable solution for PB-scale remote sensing data processing. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A DGGS-based method converts vector topology into grid-encoded set operations, reducing computational complexity for large-scale coverage selection. Results closely match those of vector methods, achieving ~100% coverage; efficiency improves by up to 11×, with level 7 grids offering the best trade-off. What are the implications of the main findings? DGGS enables a scalable spatial framework for efficient large-scale remote sensing coverage retrieval and optimization. Robust performance is maintained under sparse and partial coverage, supporting PB-scale data management and rapid preprocessing. Regional image coverage retrieval is a critical problem in large-scale remote sensing data processing. However, traditional vector topology-based methods suffer from rapidly increasing computational costs when handling massive and highly overlapping datasets. This paper proposes a coverage retrieval approach based on the Discrete Global Grid System (DGGS), which transforms geometric topological operations into grid-encoding set operations, thereby reconstructing the coverage computation process under a discrete spatial indexing framework. A heuristic greedy strategy is integrated to achieve efficient coverage selection. Experimental results demonstrate that the proposed DGGS-based method achieves speedups ranging from approximately 1.5× to 11×, depending on dataset scale and coverage. Grid-level analysis indicates that level 7 grids generally provide a favorable balance between spatial accuracy and computational efficiency for near-complete coverage retrieval, whereas level 6 grids offer a more computationally efficient alternative for rapid coverage estimation and sparse-coverage scenarios, with only marginal accuracy loss. Furthermore, the method exhibits near-linear scalability with increasing data volume and maintains stable performance under incomplete coverage scenarios. The results confirm that DGGS-based discrete modeling significantly reduces computational complexity while preserving spatial reliability, providing an efficient and scalable solution for PB-scale remote sensing data processing. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111855 |
