Beyond the frame: evaluating panoramic vs. perspective images for assessing place perception

Street View Imagery is used extensively to predict and understand the perception of the urban space by pedestrians (e.g. feeling of safety). While traditional approaches use perspective images (implying a limited field of view) to collect the opinion of participants, this paper evaluates the use of 360° imagery in crowdsourcing experiments. 360° imagery has the benefit of adding visual context compared to perspective images (‘out-of-frame’ information) and provides a better immersion for the participant, while being accessible on regular computer monitors. We developed a website to collect the opinion of 364 participants on both types of images. Based on 48 comparisons, our experiment shows that in 19% of the cases, the evaluation of the safety of a place differs if the context is shown to the participant beforehand. Additionally, we demonstrate that the out-of-frame information plays a role in how a participant rates an image. Finally, we provide comparisons with a Machine Learning model trained on the popular Place Pulse 2.0 dataset and show that using 360° imagery has a smoothing effect over the ratings of a place, compared to perspective images. These results have strong implications for the creation of urban perception datasets via crowdsourcing techniques.