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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.
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]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: 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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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2026
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