 This research aimed to develop a classification of land use-land cover, LULC, in the Paramo using satellite imagery and several classifiers, including pixel-based image analysis, PBIA, geographic object-based image analysis, Geobia, and deep neural network, DNN. The study used various parameters such as NDVI, BSI, texture, altitude, and slope to classify seven classes, including Paramo, pasture, crops, herbaceous vegetation, urban, true-brainland, and forestry plantations. The results showed that DNN had the highest overall precision of 87.43%, while Geobia achieved a precision of 95% for the Paramo class specifically. This article was authored by Marco Costello Cabri, Jose A. Piedra Fernandez, and Rosa Ayala.