Land Cover Classification from a Mapping Perspective: Pixelwise Supervision in the Deep Learning Era

Land cover classification is often only looked at from a classification perspective or either coarse or only local maps are used to teach automated approaches to map orbital images. In this work we complement a large remote sensing archive used for multi-label classification with pixel-synchronous land cover maps. The complementary annotations uncover a significant amount of wrongly labelled samples and yield novel insights into the shortcomings of multi-label based approaches. Further, it is now possible to train deep networks for land cover classification with pixel-wise supervision on a large scale.