In-Memory Distributed Mosaicking for Large-Scale Remote Sensing Applications with Geo-Gridded Data Staging on Alluxio Article Swipe
Yan Ma
,
Jie Song
,
Zhixin Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14235987
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14235987
The unprecedented availability of petascale analysis-ready earth observation data has given rise to a remarkable surge in demand for regional to global environmental studies, which exploit tons of data for temporal–spatial analysis at a much larger scale than ever. Imagery mosaicking, which is critical for forming “One Map” with a continuous view for large-scale climate research, has drawn significant concern. However, despite employing distributed data processing engines such as Spark, large-scale data mosaicking still significantly suffers from a staggering number of remote sensing images which could inevitably lead to discouraging performance. The main ill-posed problem of traditional parallel mosaicking algorithms is inherent in the huge computation demand and incredible heavy data I/O burden resulting from intensively shifting tremendous RS data back and forth between limited local memory and bulk external storage throughout the multiple processing stages. To address these issues, we propose an in-memory Spark-enabled distributed data mosaicking at a large scale with geo-gridded data staging accelerated by Alluxio. It organizes enormous “messy” remote sensing datasets into geo-encoded gird groups and indexes them with multi-dimensional space-filling curves geo-encoding assisted by GeoTrellis. All the buckets of geo-grided remote sensing data groups could be loaded directly from Alluxio with data prefetching and expressed as RDDs implemented concurrently as grid tasks of mosaicking on top of the Spark-enabled cluster. It is worth noticing that an in-memory data orchestration is offered to facilitate in-memory big data staging among multiple mosaicking processing stages to eliminate the tremendous data transferring at a great extent while maintaining a better data locality. As a result, benefiting from parallel processing with distributed data prefetching and in-memory data staging, this is a much more effective approach to facilitate large-scale data mosaicking in the context of big data. Experimental results have demonstrated our approach is much more efficient and scalable than the traditional ways of parallel implementing.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14235987
- https://www.mdpi.com/2072-4292/14/23/5987/pdf?version=1670552121
- OA Status
- gold
- Cited By
- 6
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4310184809
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4310184809Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs14235987Digital Object Identifier
- Title
-
In-Memory Distributed Mosaicking for Large-Scale Remote Sensing Applications with Geo-Gridded Data Staging on AlluxioWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-25Full publication date if available
- Authors
-
Yan Ma, Jie Song, Zhixin ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14235987Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-4292/14/23/5987/pdf?version=1670552121Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/14/23/5987/pdf?version=1670552121Direct OA link when available
- Concepts
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Computer science, SPARK (programming language), Petascale computing, Scale (ratio), Data processing, Big data, Computer data storage, Grid, Remote sensing, Scalability, Database, Data mining, Computer hardware, Cartography, Geology, Geography, Geodesy, Programming languageTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1, 2023: 4Per-year citation counts (last 5 years)
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68Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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