OpenStreetMap Data to ML Training Labels for Object Detection

So I wanted to create a seamless tutorial for taking OpenStreetMap (OSM) vector data and converting it for use with machine learning (ML) models. In particular, I am really interested in creating a tight, clean pipeline for disaster relief applications, where we can use something like crowd sourced building polygons from OSM to train a supervised object detector to discover buildings in an unmapped location. The recipe for building a basic deep learning object detector is to have two components: (1) training data (raster image vector label pairs) and (2) model framework. The deep learning model itself will be a Single Shot Detector (SSD) object detector. We will use OSM polygons as the basis of our label data and Digital Globe imagery for the raster data. We won't go into the details of an SSD here, as there are plenty sources available.