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English Information

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Authors
# Name
1 Mariana Schaefer(mariana.schaefer@ufv.br)
2 Carlos Brumatti(carlos.h.tavares@ufv.br)
3 Gustavo Veloso(gustavo.v.veloso@gmail.com)
4 Jugurta Lisboa-Filho(jugurta@ufv.br)
5 Elpídio Filho(elpidio@ufv.br)
6 Julio Reis(jreis@ufv.br)

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Reference
# Reference
1 Arancibia, G. V., Bustamante, O. P., Vigneau, G. H., Allende-Cid, H., Fuentelaba, G. S., and Nieto, V. A. (2021). Estimation of moisture content in thickened tailings dams: Machine learning techniques applied to remote sensing images. IEEE Access, 9:16988–16998.
2 Gibril, M. B. A., Idrees, M. O., Yao, K., and Shafri, H. Z. M. (2018). Integrative image segmentation optimization and machine learning approach for high quality land-use and land-cover mapping using multisource remote sensing data. Journal of Applied Remote Sensing, 12.
3 Jamali, A. (2019). Evaluation and comparison of eight machine learning models in land use/land cover mapping using landsat 8 oli: a case study of the northern region of iran. SN Applied Sciences, 1.
4 Jamali, A. (2021). Land use land cover mapping using advanced machine learning classifiers. Ekologia Bratislava, 40:286–300.
5 Keshtkar, H., Voigt, W., and Alizadeh, E. (2017). Land-cover classification and analysis of change using machine-learning classifiers and multi-temporal remote sensing imagery. Arabian Journal of Geosciences, 10.
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7 Lang, N., Jetz, W., Schindler, K., and Wegner, J. D. (2022). A high-resolution canopy height model of the earth. arXiv preprint arXiv:2204.08322.
8 Langford, Z. L., Kumar, J., Hoffman, F. M., Breen, A. L., and Iversen, C. M. (2019). Arctic vegetation mapping using unsupervised training datasets and convolutional neural networks. Remote Sensing, 11(1):69.
9 Liu, X. and Li, Y. (2021). Research on classification method of medium resolution remote sensing image based on machine learning. Lecture Notes in Computer Science, 12753 LNCS:164–173. deep learning.
10 Matinfar, H. R., Maghsodi, Z., Mousavi, S. R., and Rahmani, A. (2021). Evaluation and prediction of topsoil organic carbon using machine learning and hybrid models at a field-scale. Catena, 202.
11 Molinaro, C. A. and Leal, A. A. F. (2018). Big data, machine learning and environmental preservation: Technological instruments in defense of the environment. VEREDAS DO DIREITO, 15(31):201–224.
12 Naimi, S., Ayoubi, S., Demattˆe, J. A. M., Zeraatpisheh, M., Amorim, M. T. A., and Mello, F. (2021). Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning. Geocarto International.
13 Rostaminia, M., Rahmani, A., Mousavi, S. R., Taghizadeh-Mehrjardi, R., and Maghsodi, Z. (2021). Spatial prediction of soil organic carbon stocks in an arid rangeland using machine lefarning algorithms. Environmental Monitoring and Assessment, 193.
14 Seufitelli, D. B., Moura, A. F. C., Fernandes, A. C., Siqueira, K. M., Brand ão, M. A., and Moro, M. M. (2021). Forense digital e bancos de dados: um survey. In Simpósio Brasileiro de Bancos de Dados (SBBD), pages 307–312. SBC.
15 Vasilakos, C., Kavroudakis, D., and Georganta, A. (2020). Machine learning classification ensemble of multitemporal sentinel-2 images: The case of a mixed mediterranean ecosystem. Remote Sensing, 12.