  Urban land use on a building instance level is crucial geo-information for
many applications, yet difficult to obtain. An intuitive approach to close this
gap is predicting building functions from ground level imagery. Social media
image platforms contain billions of images, with a large variety of motifs
including but not limited to street perspectives. To cope with this issue this
study proposes a filtering pipeline to yield high quality, ground level imagery
from large social media image datasets. The pipeline ensures that all resulting
images have full and valid geotags with a compass direction to relate image
content and spatial objects from maps.
  We analyze our method on a culturally diverse social media dataset from
Flickr with more than 28 million images from 42 cities around the world. The
obtained dataset is then evaluated in a context of 3-classes building function
classification task. The three building classes that are considered in this
study are: commercial, residential, and other. Fine-tuned state-of-the-art
architectures yield F1-scores of up to 0.51 on the filtered images. Our
analysis shows that the performance is highly limited by the quality of the
labels obtained from OpenStreetMap, as the metrics increase by 0.2 if only
human validated labels are considered. Therefore, we consider these labels to
be weak and publish the resulting images from our pipeline together with the
buildings they are showing as a weakly labeled dataset.
