TY - JOUR
T1 - Scidb based framework for storage and analysis of remote sensing big data
AU - Joshi, A.
AU - Pebesma, E.
AU - Henriques, Roberto
AU - Appel, M.
N1 - Joshi, A., Pebesma, E., Henriques, R., & Appel, M. (2019). Scidb based framework for storage and analysis of remote sensing big data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(5/W3), 43-47. https://doi.org/10.5194/isprs-archives-XLII-5-W3-43-2019
PY - 2019/12/5
Y1 - 2019/12/5
N2 - Earth observation data of large part of the world is available at different temporal, spectral and spatial resolution. These data can be termed as big data as they fulfil the criteria of 3 Vs of big data: Volume, Velocity and Variety. The size of image in archives are multiple petabyte size, the size is growing continuously and the data have varied resolution and usages. These big data have variety of applications including climate change study, forestry application, agricultural application and urban planning. However, these big data also possess challenge of data storage, management and high computational requirement for processing. The solution to this computational and data management requirements is database system with distributed storage and parallel computation.In this study SciDB, an array-based database is used to store, manage and process multitemporal satellite imagery. The major aim of this study is to develop SciDB based scalable solution to store and perform time series analysis on multi-temporal satellite imagery. Total 148 scene of landsat image of 10 years period between 2006 and 2016 were stored as SciDB array. The data was then retrieved, processed and visualized. This study provides solution for storage of big RS data and also provides workflow for time series analysis of remote sensing data no matter how large is the size..
AB - Earth observation data of large part of the world is available at different temporal, spectral and spatial resolution. These data can be termed as big data as they fulfil the criteria of 3 Vs of big data: Volume, Velocity and Variety. The size of image in archives are multiple petabyte size, the size is growing continuously and the data have varied resolution and usages. These big data have variety of applications including climate change study, forestry application, agricultural application and urban planning. However, these big data also possess challenge of data storage, management and high computational requirement for processing. The solution to this computational and data management requirements is database system with distributed storage and parallel computation.In this study SciDB, an array-based database is used to store, manage and process multitemporal satellite imagery. The major aim of this study is to develop SciDB based scalable solution to store and perform time series analysis on multi-temporal satellite imagery. Total 148 scene of landsat image of 10 years period between 2006 and 2016 were stored as SciDB array. The data was then retrieved, processed and visualized. This study provides solution for storage of big RS data and also provides workflow for time series analysis of remote sensing data no matter how large is the size..
KW - Array database
KW - Big data
KW - Parallel processing
KW - Remote sensing
KW - SciDB
KW - Time series analysis
UR - http://www.scopus.com/inward/record.url?scp=85081629110&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-XLII-5-W3-43-2019
DO - 10.5194/isprs-archives-XLII-5-W3-43-2019
M3 - Conference article
AN - SCOPUS:85081629110
VL - 42
SP - 43
EP - 47
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
IS - 5/W3
T2 - 2019 International Workshop on Capacity Building and Education Outreach in Advanced Geospatial Technologies and Land Management
Y2 - 10 December 2019 through 11 December 2019
ER -
