This is an interesting area and the most important recent change is the opening of the data. More and more municipalities provide web services where you can dow

This is an interesting area and the most important recent change is the opening of the data. More and more municipalities provide web services where you can download high-quality imagery, e.g. have a look at https://www.wien.gv.at/ma41datenviewer/public/ https://www.wien.gv.at/ma41datenviewer/public/ in the left menu you can select "Luftaufnahmen" (aerial imagery) or DOM (Digitales Oberflaechenmodell == Digital Surface Model) and the resolution is quite high (unfortunately the imagery is watermark-raped, but most training methods are robust against that kind of errors). One might think aerial imagery is not a problem, as we have Google Maps, Bing, etc., but you reach there (free) API limits quite fast, if the goal is to get bulk data to train classifiers. In particular laser data is a game changer and makes building footprint detection much simpler (you can make pretty training masks from the provided vector layers and train e.g. a NN for segmentation). Current state-of-the art in building and street detection from photos only is around 85% to 95%, which is quite high, but a bit too low for fully automatic usage in e.g. urban development or environmental engineering. In particular there are images where it is nearly impossible to distinguish the roof from the nearby place, if the roof is not enclosed by a green area or a garden. At the moment classifications/segmentations are polished by humans (and providing accurate (satellite or aerial) imagery classification is a big business), but DSM data is so good, that it makes fully automatic approaches feasible and I would say one could reach ~99% there.