  Deep learning based computer vision models are increasingly used by urban
planners to support decision making for shaping urban environments. Such models
predict how people perceive the urban environment quality in terms of e.g. its
safety or beauty. However, the blackbox nature of deep learning models hampers
urban planners to understand what landscape objects contribute to a
particularly high quality or low quality urban space perception. This study
investigates how computer vision models can be used to extract relevant policy
information about peoples' perception of the urban space. To do so, we train
two widely used computer vision architectures; a Convolutional Neural Network
and a transformer, and apply GradCAM -- a well-known ex-post explainable AI
technique -- to highlight the image regions important for the model's
prediction. Using these GradCAM visualizations, we manually annotate the
objects relevant to the models' perception predictions. As a result, we are
able to discover new objects that are not represented in present object
detection models used for annotation in previous studies. Moreover, our
methodological results suggest that transformer architectures are better suited
to be used in combination with GradCAM techniques. Code is available on Github.

Computer Vision and Pattern Recognition