dr. R. Vargas Maretto (Raian)

Filho, P. S. , Persello, C. , Maretto, R. V., & MacHado, R. (2023). Investigating Sar-Optical Deep Learning Data Fusion to Map the Brazilian Cerrado Vegetation with Sentinel Data. In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings (pp. 1365-1368). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2023-July). IEEE. https://doi.org/10.1109/IGARSS52108.2023.10282190

Matosak, B. M., Fonseca, L. M. G., Taquary, E. C. , Maretto, R. V., Bendini, H. D. N., & Adami, M. (2022). Mapping Deforestation in Cerrado Based on Hybrid Deep Learning Architecture and Medium Spatial Resolution Satellite Time Series. Remote sensing, 14(1), Article 209. https://doi.org/10.3390/rs14010209

Bendini, H. N., Fonseca, L. M. G. , Maretto, R. V., Matosak, B. M., Taquary, E. C., Haidar, R. F., & Valeriano, D. D. M. (2021). Exploring a deep convolutional neural network and GEOBIA for automatic recognition of Brazilian Palm Swamps (Veredas) using Sentinel-2 optical data. In 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 5401-5404). (IEEE International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2021). IEEE. https://doi.org/10.1109/igarss47720.2021.9554050

Taquary, E. C., Fonseca, L. M. G. , Maretto, R. V., Bendini, H. N., Matosak, B. M., Sant'Anna, S. J. S., & Mura, J. C. (2021). Detecting clearcut deforestation employing deep learning methods and SAR time series. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 4520-4523). (IEEE International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2021). IEEE. https://doi.org/10.1109/igarss47720.2021.9554383

Sun, X. , Zhao, W. , V. Maretto, R. , & Persello, C. (2021). Building polygon extraction from aerial images and digital surface models with a frame field learning framework. Remote sensing, 13(22), 1-21. Article 4700. https://doi.org/10.3390/rs13224700

Fonseca, L. M. G., Körting, T. S., Bendini, H. N., Girolamo Neto, C. D., Neves, A. K., Soares, A. R., Taquary, E. C. , & Maretto, R. V. (2021). Pattern recognition and remote sensing techniques applied to land use and land cover mapping in the Brazilian Savannah. Pattern recognition letters, 148, 54-60. https://doi.org/10.1016/j.patrec.2021.04.028

Sun, X. , Zhao, W. , Maretto, R. V. , & Persello, C. (2021). Building outline extraction from aerial imagery and digital surface model with a frame field learning framework. In N. Paparoditis, C. Mallet, F. Lafarge, M. Y. Yang, A. Yilmaz, J. D. Wegner, F. Remondino, T. Fuse, & I. Toschi (Eds.), The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (B2-2021 ed., Vol. 43, pp. 487-493). (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives). International Society for Photogrammetry and Remote Sensing (ISPRS). https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-487-2021

Maretto, R. V., Fonseca, L. M. G., Jacobs, N., Korting, T. S., Bendini, H. N., & Parente, L. L. (2021). Spatio-temporal deep learning approach to map deforestation in Amazon rainforest. IEEE geoscience and remote sensing letters, 18(5), 771-775. https://doi.org/10.1109/LGRS.2020.2986407

Matosak, B. M. , Maretto, R. V., Korting, T. S., Adami, M., & Fonseca, L. M. G. (2020). Mapping deforested areas in the Cerrado Biome through recurrent neural networks. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings (pp. 1389-1392). Article 9324019 IEEE. https://doi.org/10.1109/IGARSS39084.2020.9324019

Bendini, H. N., Fonseca, L. M. G., Soares, A. R., Rufin, P., Schwieder, M., Rodrigues, M. A. , Maretto, R. V., Korting, T. S., Leitao, P. J., Sanches, I. D. A., & Hostert, P. (2020). Applying a phenological object-based image analysis (phenobia) for agricultural land classification: A study case in the Brazilian Cerrado. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings (pp. 1078-1081). Article 9323184 IEEE. https://doi.org/10.1109/IGARSS39084.2020.9323184