Spark Sensing: A Cloud Computing Framework to Unfold Processing Efficiencies for Large and Multiscale Remotely Sensed Data, with Examples on Landsat 8 and MODIS Data
Joint Authors
Lan, Hai
Zheng, Xinshi
Torrens, Paul M.
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-23
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Inquiry using data from remote Earth-observing platforms often confronts a straightforward but particularly thorny problem: huge amounts of data, in ever-replenishing supplies, are available to support inquiry, but scientists’ agility in converting data into actionable information often struggles to keep pace with rapidly incoming streams of data that amass in expanding archival silos.
Abstraction of those data is a convenient response, and many studies informed purely by remotely sensed data are by necessity limited to a small study area with a relatively few scenes of imagery, or they rely on larger mosaics of images at low resolution.
As a result, it is often challenging to thread explanations across scales from the local to the global, even though doing so is often critical to the science under pursuit.
Here, a solution is proposed, by exploiting Apache Spark, to implement parallel, in-memory image processing with ability to rapidly classify large volumes of multiscale remotely sensed images and to perform necessary analysis to detect changes on the time series.
It shows that processing on three different scales of Landsat 8 data (up to ~107.4 GB, five-scene, time series image sets) can be accomplished in 1018 seconds on local cloud environment.
Applying the same framework with slight parameter adjustments, it processed same coverage MODIS data in 54 seconds on commercial cloud platform.
Theoretically, the proposed scheme can handle all forms of remote sensing imagery commonly used in the Earth and environmental sciences, requiring only minor adjustments in parameterization of the computing jobs to adjust to the data.
The authors suggest that the “Spark sensing” approach could provide the flexibility, extensibility, and accessibility necessary to keep inquiry in the Earth and environmental sciences at pace with developments in data provision.
American Psychological Association (APA)
Lan, Hai& Zheng, Xinshi& Torrens, Paul M.. 2018. Spark Sensing: A Cloud Computing Framework to Unfold Processing Efficiencies for Large and Multiscale Remotely Sensed Data, with Examples on Landsat 8 and MODIS Data. Journal of Sensors،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1200853
Modern Language Association (MLA)
Lan, Hai…[et al.]. Spark Sensing: A Cloud Computing Framework to Unfold Processing Efficiencies for Large and Multiscale Remotely Sensed Data, with Examples on Landsat 8 and MODIS Data. Journal of Sensors No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1200853
American Medical Association (AMA)
Lan, Hai& Zheng, Xinshi& Torrens, Paul M.. Spark Sensing: A Cloud Computing Framework to Unfold Processing Efficiencies for Large and Multiscale Remotely Sensed Data, with Examples on Landsat 8 and MODIS Data. Journal of Sensors. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1200853
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1200853