  Computer vision techniques enable automated detection of sky pixels in
outdoor imagery. In urban climate, sky detection is an important first step in
gathering information about urban morphology and sky view factors. However,
obtaining accurate results remains challenging and becomes even more complex
using imagery captured under a variety of lighting and weather conditions.
  To address this problem, we present a new sky pixel detection system
demonstrated to produce accurate results using a wide range of outdoor imagery
types. Images are processed using a selection of mean-shift segmentation,
K-means clustering, and Sobel filters to mark sky pixels in the scene. The
algorithm for a specific image is chosen by a convolutional neural network,
trained with 25,000 images from the Skyfinder data set, reaching 82% accuracy
for the top three classes. This selection step allows the sky marking to follow
an adaptive process and to use different techniques and parameters to best suit
a particular image. An evaluation of fourteen different techniques and
parameter sets shows that no single technique can perform with high accuracy
across varied Skyfinder and Google Street View data sets. However, by using our
adaptive process, large increases in accuracy are observed. The resulting
system is shown to perform better than other published techniques.
