Land cover information is critical to scientific, economic, and public policy-making. There is a high demand for accurate and timely land cover information that affects the accuracy of all subsequent applications. The availability of Google Earth Engine (GEE), which derives temporal aggregation methods from time-series images (i.e., the use of metrics such as mean or median), has also enabled optimization of computation time, such as managing large amounts of data to obtain more accurate results. Our objective was to obtain a land cover map for the northwest of the province of Córdoba, Argentina. The study was carried out in rural communities that belong to the departments of Cruz del Eje and Ischilín, northwest of Córdoba, and have different degrees of intervention in the land cover. Sentinel 2 Level 2A images were acquired for the study area. Images available from January 1, 2018, to December 31, 2020, were sampled. To create a thematic map, the median value was calculated for the sample of images from the selected time interval. Finally, the Normalized Difference Vegetation Index (NDVI) was calculated and added to the total bands of the median image. Training polygons were placed there considering the visual features in the median image. The Random Forest algorithm was used as the classification method. To verify the quality of the classified map, a list of 97,753 verification pixels was obtained. In addition, a confusion matrix was created to collect the conflicts that arise between categories, and the precision and kappa coefficient was calculated to define the quality of the map obtained. Image acquisition, preprocessing, and analysis were performed on the Google Earth Engine platform. Thematic maps with eight classes were obtained, with a total area of 719880 ha. The confusion matrix showed an overall precision of 99.26% and a corrected kappa index of 0.99, the classes were correctly classified by the algorithm.