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., Mané, 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., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X., 2015. TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org. http://tensorflow.org/.
2. An integrated approach of analytical network process and fuzzy based spatial decision making systems applied to landslide risk mapping;Abedi Gheshlaghi;J. Afr. Earth Sc.,2017
3. Topography-driven satellite imagery analysis for landslide mapping;Alvioli;Geomatics, Natural Hazards Risk,2018
4. Towards operational satellite-based damage-mapping using U-Net convolutional network: a case study of 2011 Tohoku;Bai;Remote Sensing,2018
5. Bragagnolo, L., da Silva, R., Grzybowski, J., 2020a. Artificial neural network ensembles applied to the mapping of landslide susceptibility. Catena 184 (January 2019), 104240. doi: 10.1016/j.catena.2019.104240.