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.

我们的研究在阿姆斯特丹市通过对地面和空中影像的数据和模型进行测试，以识别住房质量。结果显示，相较于仅使用空中影像，Google StreetView（GSV）能够预测出最准确的建筑质量分数，提高了约 30%。然而，通过仔细筛选和使用正确的预训练模型，将 Flickr 图像特征与空中图像特征相结合，能将性能差距从 30% 降低至 15%。这表明在可居住性因素预测方面，存在可行的替代方法，这是令人鼓舞的，因为 GSV 图像难以获取并且并非总是可用。