  Excessive or inadequate exposure to ultraviolet light (UV) is harmful to
health and causes osteoporosis, colon cancer, and skin cancer. The UV Index, a
standard scale of UV light, tends to increase in sunny places and sharply
decrease in the shade. A method for distinguishing shady and sunny places would
help us to prevent and cure diseases caused by UV. However, the existing
methods, such as carrying UV sensors, impose a load on the user, whereas
city-level UV forecasts do not have enough granularity for monitoring an
individual's UV exposure. This paper proposes a method to detect sunny and
shady places by using an off-the-shelf mobile device. The method detects these
places by using a characteristic of the GNSS signal strength that is attenuated
by objects around the device. As a dataset, we collected GNSS signal data, such
as C/N0, satellite ID, satellite angle, and sun angle, together with reference
data (i.e., sunny and shady place information every minute) for four days from
five locations. Using the dataset, we created twelve classification models by
using supervised machine learning methods and evaluated their performance by
4-fold cross-validation. In addition, we investigated the feature importance
and the effect of combining features. The performance evaluation showed that
our classification model could classify sunny and shady places with more than
97% accuracy in the best case. Moreover, our investigation revealed that the
value of C/N0 at a moment and its time series (i.e., C/N0 value before and
after the moment) are more important features.
