@inproceedings{cbb38a2865e542e28d34e5f47177c51b,
title = "Cross-Modal Learning of Housing Quality in Amsterdam",
abstract = "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. ",
keywords = "deep learning, housing, liveability, remote sensing, urban",
author = "Alex Levering and Diego Marcos and Ilan Havinga and Devis Tuia",
year = "2021",
month = nov,
day = "2",
doi = "10.1145/3486635.3491067",
language = "English",
series = "Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021",
pages = "1--4",
editor = "Dalton Lunga and Lexie Yang and Song Gao and Bruno Martins and Yingjie Hu and Xueqing Deng and Shawn Newsam",
booktitle = "Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021",
note = "4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI 2021 ; Conference date: 02-11-2021 Through 02-11-2021",
}
