Cross-Modal Learning of Housing Quality in Amsterdam
<|reference_start|>Cross-Modal Learning of Housing Quality in Amsterdam: 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.<|reference_end|>
@article{levering2024cross-modal,
    title={Cross-Modal Learning of Housing Quality in Amsterdam},
    author={Alex Levering, Diego Marcos, Devis Tuia},
    journal={arXiv preprint arXiv:2403.08915},
    year={2024},
    doi={10.1145/3486635.3491067},
    archivePrefix={arXiv},
    eprint={2403.08915},
    primaryClass={cs.CV cs.AI}
}