pyjeo: A Python Package for the Analysis of Geospatial Data

A new Python package, pyjeo, that deals with the analysis of geospatial data has been created by the Joint Research Centre (JRC). Adopting the principles of open science, the JRC strives for transparency and reproducibility of results. In this view, it has been decided to release pyjeo as free and o...

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Bibliographic Details
Published inISPRS international journal of geo-information Vol. 8; no. 10; p. 461
Main Authors Kempeneers, Pieter, Pesek, Ondrej, De Marchi, Davide, Soille, Pierre
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 17.10.2019
Subjects
Access to information
Algorithms
Analysis
Batch processing
C plus plus
C++ (programming language)
Cloud computing
computer software
Cubes
Data
Data analysis
Data models
Design
geospatial data
Image analysis
Image processing
Libraries
Open access
Open data
Open source software
Programming languages
Prototyping
Python
Reproducibility
Research facilities
Science
Scientists
Spatial data
Time series
User interface
Wavelet transforms
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ISSN2220-9964
2220-9964
DOI10.3390/ijgi8100461

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Summary:A new Python package, pyjeo, that deals with the analysis of geospatial data has been created by the Joint Research Centre (JRC). Adopting the principles of open science, the JRC strives for transparency and reproducibility of results. In this view, it has been decided to release pyjeo as free and open software. This paper describes the design of pyjeo and how its underlying C/C++ library was ported to Python. Strengths and limitations of the design choices are discussed. In particular, the data model that allows the generation of on-the-fly data cubes is of importance. Two uses cases illustrate how pyjeo can contribute to open science. The first is an example of large-scale processing, where pyjeo was used to create a global composite of Sentinel-2 data. The second shows how pyjeo can be imported within an interactive platform for image analysis and visualization. Using an innovative mechanism that interprets Python code within a C++ library on-the-fly, users can benefit from all functions in the pyjeo package. Images are processed in deferred mode, which is ideal for prototyping new algorithms on geospatial data, and assess the suitability of the results created on the fly at any scale and location.
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ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi8100461