  In our research we test data and models for the recognition of housing
quality in the city of Amsterdam from ground-level and aerial imagery. For
ground-level images we compare Google StreetView (GSV) to Flickr images. Our
results show that GSV predicts the most accurate building quality scores,
approximately 30% better than using only aerial images. However, we find that
through careful filtering and by using the right pre-trained model, Flickr
image features combined with aerial image features are able to halve the
performance gap to GSV features from 30% to 15%. Our results indicate that
there are viable alternatives to GSV for liveability factor prediction, which
is encouraging as GSV images are more difficult to acquire and not always
available.
