Volume IV-5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5, 29-36, 2018
https://doi.org/10.5194/isprs-annals-IV-5-29-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-5, 29-36, 2018
https://doi.org/10.5194/isprs-annals-IV-5-29-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Nov 2018

15 Nov 2018

ONLINE GEOPROCESSING USING MULTI-DIMENSIONAL GRIDDED DATA

A. Kiran, P. K. Gupta, A. K. Jha, and S. Saran A. Kiran et al.
  • Geoinformatics Department, Indian Institute of Remote Sensing, Dehradun, India

Keywords: Data Cube, Geoprocessing, Python, LISS III Data, NDVI, Online Spatial Analysis

Abstract. Traditional geoprocessing techniques often rely on the use of multiple softwares for data handling and management which consumes almost 80% of the time and requires the user to be well versed with all the intricacies of pre-processing. Therefore, there is a need to reverse the trend on analysis and data management, so as to enable scientists and researchers to focus on the science rather than data handling and pre-processing. The concept of a Data Cube which is a massive multi-dimensional array of raster or gridded data, ‘stacks’ satellite images and addresses the problems faced by traditional remote sensing practices and provides an interactive environment where datasets can be analysed with relative ease as compared to its traditional counterparts. This framework allows multi-format and multi-projection datasets spanning decades to be used in various geoprocessing techniques from simple GIS tasks such as data conversion, time series generation, and to do more complex tasks such as change detection, NDVI generation, unsupervised classification and modelling. LISS III data for the state of Uttarakhand, India was used on an interactive interface called the Jupyter Notebook where scripts written in Python allowed data to be ingested, analysed and visualised. The Data Cube framework hence proved to be a flexible and extensive development environment which can be extended to meet more complex modelling requirements.