Cross-Modal Learning of Housing Quality in Amsterdam
Alex Levering Diego Marcos Devis Tuia
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.