Brazil Data Cube Code Gallery

This repository contains a gallery of interesting Jupyter Notebooks, R Markdown and scripts based on Brazil Data Cube data and technologies.

Jupyter Notebooks - Services

Data Access through SpatioTemporal Asset Catalog API (STAC)

• Introduction to the SpatioTemporal Asset Catalog. (Python, R)

• SpatioTemporal Asset Catalog and Geometries. (Python).

• Image processing on images obtained through STAC. (Python).

Web Time Series Service (WTSS)

• Introduction to the Web Time Series Service (WTSS). (Python, R).

• Web Time Series Service (WTSS) examples. (Python, R).

• Web Time Series Service 2 - Time series from geometry. (Python).

Web Land Trajectory Service (WLTS)

• Introduction to the Web Land Trajectory Service (WLTS). (Python, R)

• Web Land Trajectory Service (WLTS) examples. (Python).

Web Crop Phenology Metrics Service (WCPMS)

• Introduction to the Web Crop Phenology Metrics Service (WCPMS). (Python).

• Web Crop Phenology Metrics Service (WCPMS) Region. (Python).

Land Cover Classification System Service (LCCS)

• Introduction to the Land Cover Classification System Service (LCCS). (Python)

Sample Database (SAMPLE-DB)

• Introduction to the Sample Database (SAMPLE-DB). (Python)

Tile Map Service (BDC-Tiler)

• Introduction to the Tile Map Service (BDC-Tiler). (Python)

Mapping LULC using BDC Data Cubes

• Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products. (R)

Events

BIG TechTalks

• Processamento de Imagens GOES utilizando STAC e Python

• Acesso, Visualização e Processamento de Imagens Sentinel-2 utilizando Python - Spatio Temporal Asset Catalog (STAC)

• Sensoriamento Remoto aplicado ao estudo de ecossistemas de água doce

• Acesso e visualização de dados TOPODATA com Python e STAC

• Visualização e Extração de Séries Temporais de Valores de Radiação Solar Estimados pelo Modelo GL1.2

• Love Data Day 2025

• Love Data Day 2026

Other Events

Articles

Mapping LULC using BDC Data Cubes

• Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products. (R)

Share & cite this article

Plain text

Ferreira, K.R.; Queiroz, G.R.; Vinhas, L.; Marujo, R.F.B.; Simoes, R.E.O.; Picoli, M.C.A.; Camara, G.; Cartaxo, R.; Gomes, V.C.F.; Santos, L.A.; Sanchez, A.H.; Arcanjo, J.S.; Fronza, J.G.; Noronha, C.A.; Costa, R.W.; Zaglia, M.C.; Zioti, F.; Korting, T.S.; Soares, A.R.; Chaves, M.E.D.; Fonseca, L.M.G. Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products. Remote Sens. 2020, 12, 4033. https://doi.org/10.3390/rs12244033

BibTeX

@Article{Ferreira2020,
    author = {Ferreira, Karine R. and Queiroz, Gilberto R. and Vinhas, Lubia and Marujo, Rennan F. B. and Simoes, Rolf E. O. and Picoli, Michelle C. A. and Camara, Gilberto and Cartaxo, Ricardo and Gomes, Vitor C. F. and Santos, Lorena A. and Sanchez, Alber H. and Arcanjo, Jeferson S. and Fronza, José Guilherme and Noronha, Carlos Alberto and Costa, Raphael W. and Zaglia, Matheus C. and Zioti, Fabiana and Korting, Thales S. and Soares, Anderson R. and Chaves, Michel E. D. and Fonseca, Leila M. G.},
    title = {Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products},
    journal = {Remote Sensing},
    volume = {12},
    year = {2020},
    number = {24},
    article-number = {4033},
    url = {https://www.mdpi.com/2072-4292/12/24/4033},
    issn = {2072-4292},
    doi = {10.3390/rs12244033}
}