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.
@article{arxiv.2403.08915,
title = {Cross-Modal Learning of Housing Quality in Amsterdam},
author = {Alex Levering and Diego Marcos and Devis Tuia},
journal= {arXiv preprint arXiv:2403.08915},
year = {2024}
}