  Ensuring urban safety is an essential part of developing sustainable cities.
An urban safety map can assist cities to prevent future crimes. However,
mapping is costly in terms of both time and money due to the need for manual
data collection. On the other hand, satellite imagery is becoming increasingly
abundant and accessible with higher resolution. Given the outstanding success
deep learning has achieved in the field of computer vision and pattern
recognition over the past 5 years, in this paper we attempt to investigate the
use of deep learning to predict crime rate directly from raw satellite imagery.
To this end, we have trained a deep Convolutional Neural Network on satellite
images obtained from over one million crime-incident reports collected by the
Chicago Police Department. The best performing model predicted crime rate from
raw satellite imagery with an accuracy of 79%. To test their reusability, we
used the learned Chicago models to predict for the cities of Denver and San
Francisco city-scale maps indicating crime rate in three different levels.
Compared to maps made from years' worth of data collected by the corresponding
police departments, our maps have an accuracy of 72% and 70%, respectively.
