I highly recommend you check out Spacenet. As quoted on their website: "SpaceNet delivers access to high-quality geospatial data for developers, researchers, and startups. Before SpaceNet, computer vision researchers had minimal options to obtain free, precision-labeled, and high-resolution satellite imagery. SpaceNet focuses on four open source key pillars: data, challenges, algorithms, and tools."
They have open source labelled datasets that are covered under the creative commons license. They also hold regular Machine Learning Competitions on those datasets, and then open source and analyze the best models.
I have personally used their open source cresi road segmentation library, and was able to get some great results out of it. They also have an open source Geospatial Machine Learning Pipelining Library called Solaris which is also great.
While they do not cover Landcover Detection (Yet), I found that reading their blogposts really helped me into getting familiar with how to apply machine learning specifically on GIS data.