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Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1038\/s41597-020-0371-4","article-title":"Land Use and Cover Maps for Mato Grosso State in Brazil from 2001 to 2017","volume":"7","author":"Simoes","year":"2020","journal-title":"Sci. Data"},{"key":"ref_15","first-page":"012035","article-title":"Leveraging Time-Series Imageries and Open Source Tools for Enhanced Land Cover Classification","volume":"Volume 1276","author":"Hadi","year":"2023","journal-title":"Proceedings of the IOP Conference Series: Earth and Environmental Science"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1080\/20964471.2024.2323241","article-title":"Time-First Approach for Land Cover Mapping Using Big Earth Observation Data Time-Series in a Data Cube\u2014A Case Study from the Lake Geneva Region (Switzerland)","volume":"8","author":"Giuliani","year":"2024","journal-title":"Big Earth Data"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Werner, J.P.S., Belgiu, M., Bueno, I.T., Dos Reis, A.A., Toro, A.P.S.G.D., Antunes, J.F.G., Stein, A., Lamparelli, R.A.C., Magalh\u00e3es, P.S.G., and Coutinho, A.C. (2024). Mapping Integrated Crop\u2013Livestock Systems Using Fused Sentinel-2 and PlanetScope Time Series and Deep Learning. Remote Sens., 16.","DOI":"10.3390\/rs16081421"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Simoes, R., Camara, G., Queiroz, G., Souza, F., Andrade, P.R., Santos, L., Carvalho, A., and Ferreira, K. (2021). Satellite Image Time Series Analysis for Big Earth Observation Data. Remote Sens., 13.","DOI":"10.3390\/rs13132428"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Appel, M., and Pebesma, E. (2019). On-Demand Processing of Data Cubes from Satellite Image Collections with the Gdalcubes Library. Data, 4.","DOI":"10.32614\/CRAN.package.gdalcubes"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., and Rubin, D.B. (2014). Bayesian Data Analysis, CRC Press. [3rd ed.].","DOI":"10.1201\/b16018"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Carlin, B., and Louis, T. (2000). Bayes and Empirical Bayes Methods for Data Analysis, CRC Press.","DOI":"10.1201\/9781420057669"},{"key":"ref_22","unstructured":"Van der Vaart, A. (2000). Asymptotic Statistics, Cambridge University Press."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dwyer, J.L., Roy, D.P., Sauer, B., Jenkerson, C.B., Zhang, H.K., and Lymburner, L. (2018). Analysis Ready Data: Enabling Analysis of the Landsat Archive. Remote Sens., 10.","DOI":"10.20944\/preprints201808.0029.v1"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"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., and Santos, L.A. (2020). Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products. Remote Sens., 12.","DOI":"10.3390\/rs12244033"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1080\/20964471.2017.1398903","article-title":"Building an Earth Observations Data Cube: Lessons Learned from the Swiss Data Cube (SDC) on Generating Analysis Ready Data (ARD)","volume":"1","author":"Giuliani","year":"2017","journal-title":"Big Earth Data"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pelletier, C., Webb, G.I., and Petitjean, F. (2019). Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series. Remote Sens., 11.","DOI":"10.3390\/rs11050523"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"114107","DOI":"10.1016\/j.rse.2024.114107","article-title":"Evaluating Deep Learning Methods Applied to Landsat Time Series Subsequences to Detect and Classify Boreal Forest Disturbances Events: The Challenge of Partial and Progressive Disturbances","volume":"306","author":"Perbet","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R., and Ifrim, G. (2020). Lightweight Temporal Self-attention for Classifying Satellite Images Time Series. Advanced Analytics and Learning on Temporal Data, Proceedings of the 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, 18 September 2020, Springer.","DOI":"10.1007\/978-3-030-65742-0"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A Mathematical Theory of Communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_30","unstructured":"Camara, G., Picoli, M., Simoes, R., Maciel, A., Carvalho, A., Coutinho, A., Esquerdo, J., Antunes, J., Begotti, R., and Arvor, D. (2019). Land Cover Change Maps for Mato Grosso State in Brazil: 2001\u20132016. 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