A primer on understanding Google Earth Engine APIs

Autores

  • Rui Reis ISEL - Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa; NovaLincs, FCT – Universidade Nova de Lisboa
  • Nuno Datia ISEL http://orcid.org/0000-0003-1600-0227 (não autenticado)
  • Matilde Pato ISEL - Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa; Instituto de Biofı́sica e Engenharia Biomédica, FC-UL

DOI:

https://doi.org/10.34629/ipl.isel.i-ETC.81

Palavras-chave:

Google Earth Engine, Javascript, Python

Resumo

This article is build on the experience of using Google Earth Engine as a development framework for a previous work by the same authors.
Being primarily a distributed parallel computing platform, it is designed around a functional language pattern, even though supported on an object model, and a map / reduce distributed workload paradigm.
Leveraging the sheer computing power delivered by the Google infrastructure and a multi petabyte remote sensing data repository, Google
Earth Engine is an efficient development framework that presents itself in two basic flavors: one online integrated development environment which uses the browser Javascript engine; two APIs that can be deployed to a Python or a NodeJS environment.
This work emphasizes the comparison between the Javascript browser
based implementation and the Python environment packages.

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Biografia do Autor

  • Nuno Datia, ISEL

    ORCID ID: 0000-0003-1600-0227

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    2015 - Present: Adjunt Professor, ISEL, Lisbon, Portugal

    2002 - 2015: Teaching Assistant, ISEL, Lisbon, Portugal

    2015: Ph.D in Computer Science (UNL, Lisbon, Portugal)

    2006: MSc in Computer Science (UNL, Lisbon, Portugal)

    2002: Licenciate in Computer Science Engineering (ISEL, Lisbon, Portugal)  

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Referências

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ESA. Sentinel 2 user guide, 2019. [Online; accessed 2019-01-15].

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Google. Google earth engine guides, band math, 2020. [Online;accessed 2020-01-15].

Noel Gorelick, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202:18–27, 2017.

Python Software Foundation. pickle — python object serialization. https://docs.python.org/3/library/pickle.html, 2019. [Online; accessed 2019-06-10].

Rui S. Reis, Celia Gouveia, Nuno Datia, and M. P. M. Pato. Modelo preditivo de recuperação da vegetacão afetada por incêndios florestais. In INForum 2019 Atas do 11o Simpósio de Informática, page 461–472. NOVA.FCT Editorial, 2019.

Wikipedia contributors. World geodetic system — Wikipedia, the free encyclopedia, 2020. [Online; accessed 2020-02-03].

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Publicado

2020-10-19

Edição

Vol. 6 N.º 1 (2020): Volume 6

Secção

Master Thesis 2019

Licença

Authors of articles published in the ISEL Academic Journal of Electronics, Telecommunications and Computers retain copyright of their work, licensing it under the Creative Commons Attribution-NonCommercial 3.0 Unported License. This license allows free download of the articles from the i-ETC website, as well as re-use and re-distribution without restriction, as long as the original work is properly cited and not used for commercial purposes.

http://creativecommons.org/licenses/by-nc/4.0/