How to create a land cover model for South America in 4 steps

Recently, Radiant Earth Foundation released a land cover dataset for South America, continuing the work they had been doing in other parts of the world and in connection with other areas of interest. In connection with previous posts, this article explains how to train a segmentation model based on this dataset in just 4 steps. Specifically, we will explain in detail how to train a model for classifying the use of cropland, based on the mentioned dataset. The released dataset comprises labels and satellite imagery from Sentinel-1, Sentinel-2 and Landsat 8 missions for classifying the uses of South American land (if you would like to learn more about satellite imagery sources, click here). Each pixel is identified as one of the possible seven land classes: water, natural bare ground, artificial bare ground, woody vegetation, cultivated ground, semi-cultivated ground, and permanent snow/ice.