Scidb based framework for storage and analysis of remote sensing big data

A. Joshi, E. Pebesma, Roberto Henriques, M. Appel

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)
25 Downloads (Pure)

Abstract

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.

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Original languageEnglish
Pages (from-to)43-47
Number of pages5
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume42
Issue number5/W3
DOIs
Publication statusPublished - 5 Dec 2019
Event2019 International Workshop on Capacity Building and Education Outreach in Advanced Geospatial Technologies and Land Management - Dhulikhel, Nepal
Duration: 10 Dec 201911 Dec 2019

Keywords

  • Array database
  • Big data
  • Parallel processing
  • Remote sensing
  • SciDB
  • Time series analysis

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