  Safety perception measurement has been a subject of interest in many cities
of the world. This is due to its social relevance, and to its effect on some
local economic activities. Even though people safety perception is a subjective
topic, sometimes it is possible to find out common patterns given a restricted
geographical and sociocultural context. This paper presents an approach that
makes use of image processing and machine learning techniques to detect with
high accuracy urban environment patterns that could affect citizen's safety
perception.
