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Produção, visualização e análise de grandes volumes de imagens de sensoriamento remoto modeladas como cubos de dados multidimensionais para todo o território brasileiro.

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O Data Cube Explorer é um portal web para visualização de cubos de dados, coleções de imagens e classificações.

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Generating analysis ready data collections for Brazil

by Rennan F. B. Marujo¹, Karine R. Ferreira¹, Gilberto R. Queiroz¹, Raphael W. Costa¹, Jeferson S. Arcanjo¹, Ricardo C. M. Souza¹

National Institute for Space Research (INPE), Avenida dos Astronautas, 1758, Jardim da Granja, Sao Jose dos Campos, SP 12227-010, Brazil

DOI: https://doi.org/10.1109/IGARSS46834.2022.9884104

Publisher: IEEE  | 28 September 2022

Abstract

Brazil is a country of continental scale area, with a territory of over 8.5 millions of km². Nowadays, data from different satellites and sensors with distinct spatial, temporal, and spectral resolutions are available for free. However, prepare and handle these large amounts of data is an exhaustive task.
The Brazil Data Cube (BDC) Project emerges in this context processing and preparing Analysis Ready Data (ARD) of medium spatial resolution satellite sensors, condensing it as image data cubes for the Brazilian territory, allowing researchers to easily extract time series from them, focusing on the analysis instead of the processing. This paper describes the workflow to generate BDC ARD input data, which are used as input for data cubes, as well as point our learnings and findings handling and processing these data.

Keywords:  Analysis Ready Data, Remote Sensing, Surface Reflectance.

© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Share and Cite

Marujo, R. F. B.; Ferreira, K. R.; Queiroz, G. R.; Costa, R. W.; Arcanjo, J. S.; Souza, R. C. M.: Generating Analysis Ready Data Collections for Brazil. International Geoscience and Remote Sensing Symposium (IGARSS). V 42. https://doi.org/10.1109/IGARSS46834.2022.9884104, Jul 2022.

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