1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X., 2015. TensorFlow: Large-scale machine learning on heterogeneous systems. URL: https://www.tensorflow.org/. software available from tensorflow.org.
2. TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping;Assis;ISPRS Int. J. Geo-Inf. doi:,2019
3. Badrinarayanan, V., Kendall, A., Cipolla, R., 2016. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. arXiv:1511.00561.
4. Change Detection of Deforestation in the Brazilian Amazon Using Landsat Data and Convolutional Neural Networks;de Bem;Remote Sens.,2020
5. Bertels, J., Eelbode, T., Berman, M., Vandermeulen, D., Maes, F., Bisschops, R., Blaschko, M.B., 2019. Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory and Practice. Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 92-100 URL: https://doi.org/10.1007/978-3-030-32245-8_11, doi:10.1007/978-3-030-32245-8_11.