no code implementations • 25 Jul 2022 • Javier Caicedo, Pamela Acosta, Romel Pozo, Henry Guilcapi, Christian Mejia-Escobar
The strategy combines a Generative Adversarial Neural Network (GAN) that is trained on a dataset of aerial photographs of natural and urban landscapes to improve image resolution; a Convolutional Neural Network (CNN) trained on a dataset of aerial photographs of sugar cane plots to distinguish populated or unpopulated crop areas; and a standard image processing module for the calculation of areas in a percentage manner.
