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.

We have the pleasure of introducing Radiant Earth Foundation's first online course, Machine Learning for Earth Observations (ML4EO) Bootcamp. Available on Atingi, an open digital learning platform designed to improve training and employment opportunities, this self-paced course contains a mixture of lectures and hands-on exercises for novice data science or remote sensing practitioners. Atingi is implemented by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) on behalf of the German Federal Ministry for Economic Cooperation and Development (BMZ). A discussion exchange forum has been set up for participants to post questions about the course content and get help from others taking the course. The Radiant MLHub LinkedIn community page and Slack channel can also be used to crowdsource answers to questions.