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Medindo o Brasil a partir do espaço: tecnologias de big data e inteligência artificial

Karine Reis Ferreira, Gilberto Ribeiro de Queiroz

Resumo


A Dr.ª Karine Reis Ferreira e o Dr. Gilberto Ribeiro de Queiroz respondem questões sobre o projeto de pesquisa Brazil Data Cube, sua finalidade, impacto e relação com inteligência artificial e big data.

Palavras-chave


Big data; Artificial intelligence; Technological innovation; Data cube; Brazil Data Cube

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Referências


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. & et al. (2020a). Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products. Remote Sensing. 2020,12, 4033, doi:10.3390/rs12244033

Ferreira, K. R., Queiroz, G. R., Camara G., Souza, R. C. M., Vinhas, L., Marujo, R. F. B., Simoes, R. E. O., Noronha, C. A. F., Costa, R. W. & et al. (2020b). Using Remote Sensing Images and Cloud Services on AWS to Improve Land Use and Cover Monitoring. In: LAGIRS 2020: 2020 Latin American GRSS & ISPRS Remote Sensing Conference. Santiago de Chile, Chile, March 22-26.

Giuliani, G., Chatenoux, B., De Bono, A., Rodila, D., Richard, J.P., Allenbach, K., Dao, H. & Peduzzi, P. (2017). Building an Earth Observations Data Cube: Lessons Learned from the SwissData Cube (SDC) on Generating Analysis Ready Data (ARD). Big Earth Data, 1, 100–117, doi:10.1080/20964471.2017.1398903.

Gomes, V.C., Queiroz, G.R. & Ferreira, K.R. (2020). An Overview of Platforms for Big Earth Observation DataManagement and Analysis. Remote Sensing, 12, 1253. doi: 10.3390/rs12081253.

Gomes, V. C. F., Carlos, F. M., Queiroz, G. R., Ferreira, K. R., & Santos, R. (2021). ACCESSING AND PROCESSING BRAZILIAN EARTH OBSERVATION DATA CUBES WITH THE OPEN DATA CUBE PLATFORM, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2021, 153–159, doi: 10.5194/isprs-annals-V-4-2021-153-2021.

Killough, B. & et al. (2019) The Impact of Analysis Ready Data in the Africa Regional Data Cube. In Proceedings of the IGARSS 2019—2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July–2 August 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 5646–5649, doi:10.1109/IGARSS.2019.8898321

Lewis, A., Oliver, S., Lymburner, L., Evans, B., Wyborn, L., Mueller, N., Raevksi, G., Hooke, J., Woodcock, R., Sixsmith, J. & et al. (2017). The Australian Geoscience Data Cube—Foundations and Lessons Learned. Remote Sens. Environ., 202, 276–292, doi:10.1016/j.rse.2017.03.015.13.

Picoli, M. C. A., Simoes, R., Chaves, M., Santos, L. A., Sanchez, A., Soares, A., Sanches, I. D., Ferreira, K. R. & Queiroz, G. R. (2020). CBERS DATA CUBE: A POWERFUL TECHNOLOGY FOR MAPPING AND MONITORING BRAZILIAN BIOMES. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. , v.V-3-2020, p.533 – 539, doi: 10.5194/isprs-annals-V-3-2020-533-2020

Santos, L.A., Ferreira, K.R., Camara, G., Picoli, M.C.A. & Simoes, R.E. (2021a). Quality Control and Class Noise Reduction of Satellite Image Time Series. ISPRS J. Photogramm. Remote Sens. 2021, 177, 75–88, doi:10.1016/j.isprsjprs.2021.04.014.

Santos, L.A., Ferreira, K.R, Picoli, M., Camara, G., Zurita-Milla, R. & Augustijn, E.-W. (2021b). Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series. In: Remote Sensing Journal. v. 13, p. 974, 2021. doi: 10.3390/rs13050974

Simoes, R., Camara, G., Queiroz, G., Souza, F., Andrade, P.R., Santos, L., Carvalho, A. & Ferreira, K. (2021). Satellite Image Time Series Analysis for Big Earth Observation Data. Remote Sensing. v.13, p.2428, doi: 10.3390/rs13132428.

Soille, P., Burger, A., De Marchi, D., Kempeneers, P., Rodriguez, D., Syrris, V. & Vasilev, V. (2018) A versatile data-intensive computing platform for information retrieval from big geospatial data. Future Generation Computer Systems, v. 81, p. 30–4, doi: 10.1016/j.future.2017.11.007.




DOI: http://dx.doi.org/10.5380/atoz.v10i3.81966

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