Investigating changes in forest cover has been an area of intense research for decades. From manual surveys to remote sensing we have come a long way in mapping the world around us. Machine learning and its' younger sibling, deep learning, have emerged as highly useful tools on this journey. There are a wide variety of algorithms that take a stab at analysing vegetation coverage, but not all of them are easily accessible to the community at large. This was the underlying incentive for the paper you are about to read - reproducing and replicating a novel deep-learning algorithm that generates maps of forest cover in a relatively under-explored part of the world. This study lays out the methodology devised by the original authors as well as our attempts to replicate their work on a different set of data. We present the results we were able to obtain, the hurdles we encountered along the way, and a set of guidelines we feel would be helpful for future researchers.