Wekun (wekun.ing.puc.cl) is a game that seeks to understand how people perceive public space and, thus, understand what determines the quality of these spaces.

We ask people to chose between images to measure their preferences. Then, the information collected is processed with Machine Learning algorithms and discrete choice models, in order to understand the role played by different elements of the built environment and nature in the preferences of people. The methodology is not new (links below), but we have incorporated a section to register sociodemographic information aiming to find heterogeneity among observers. We use Discrete Choice Modelling as a benchmark to Machine Learning Algorithms, typically referred to as black boxes, to overcome the explainability problems involved with them.

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https://i.redd.it/xamzwiv0a9231.jpg

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Please comment on the following subjects to help us!

1. Any recommendation of semantic segmentation algorithms? or Object detection?
2. For the success of this research, we need your help evaluating photos of public spaces and sharing this message to have the opinion of more people!

Some references:

Rossetti, T., Lobel, H., Rocco, V., & Hurtubia, R. (2019). Explaining subjective perceptions of public spaces as a function of the built environment: A massive data approach. Landscape and urban planning, 181, 169-178. (link)

Salesses, P., Schechtner, K., & Hidalgo, C. A. (2013). The collaborative image of the city: mapping the inequality of urban perception. PloS one, 8(7), e68400. (link)

Dubey, A., Naik, N., Parikh, D., Raskar, R., & Hidalgo, C. A. (2016, October). Deep learning the city: Quantifying urban perception at a global scale. In European conference on computer vision (pp. 196-212). Springer, Cham. (link)