AmazonNET by Mohla et al. (2020) is new a model that detects and segments rainforest burn marks due to wildfires from satellite imagery. Slash-and-burn is a popular technique for clearing away large swaths of the Amazon rainforest for farming use, which additionally is a common cause of wildfires in the region. Detecting and controlling these fires from the ground is difficult, so this is very important work: automated segmentation of these burn scars will allow for more complete and objective tracking, and for more effective prevention. The most interesting contribution here is that the proposed model learns from a dataset with very noisy labels.