Técnicas de Mineração de Dados e Aprendizado de Máquina Aplicadas a Dados Ambientais de Uso e Cobertura de Solo
Resumo
Este trabalho descreve o processo de uma Revisão Sistemática da Literatura (RSL) realizada na área de mineração de dados e aprendizado de máquina aplicados a dados ambientais e geográficos com foco em cobertura e uso de solo. As publicações mais relevantes obtidas ao final deste processo são detalhadas, buscando-se elencar direções futuras para pesquisas neste contexto.
Palavras-chave:
mineração de dados, aprendizado de máquina, dados ambientais, sensoriamento remoto, revisão sistemática da literatura, RSL
Referências
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.
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.
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.
Jamali, A. (2021). Land use land cover mapping using advanced machine learning classifiers. Ekologia Bratislava, 40:286-300.
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.
Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., and Linkman, S. (2009). Systematic literature reviews in software engineering-a systematic literature review. Information and software technology, 51(1):7-15.
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.
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.
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.
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.
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.
Naimi, S., Ayoubi, S., Demattê, 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.
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.
Seufitelli, D. B., Moura, A. F. C., Fernandes, A. C., Siqueira, K. M., Brandão, 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.
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.
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.
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.
Jamali, A. (2021). Land use land cover mapping using advanced machine learning classifiers. Ekologia Bratislava, 40:286-300.
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.
Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., and Linkman, S. (2009). Systematic literature reviews in software engineering-a systematic literature review. Information and software technology, 51(1):7-15.
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.
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.
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.
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.
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.
Naimi, S., Ayoubi, S., Demattê, 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.
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.
Seufitelli, D. B., Moura, A. F. C., Fernandes, A. C., Siqueira, K. M., Brandão, 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.
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.
Publicado
19/09/2022
Como Citar
SCHAEFER, Mariana Albuquerque Reynaud; BRUMATTI, Carlos H. T.; VELOSO, Gustavo V.; LISBOA-FILHO, Jugurta; FERNANDES FILHO, Elpídio Inácio; REIS, Julio C. S..
Técnicas de Mineração de Dados e Aprendizado de Máquina Aplicadas a Dados Ambientais de Uso e Cobertura de Solo. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 37. , 2022, Búzios.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 361-366.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2022.225356.
