  Rapid identification and response to breaking events, particularly those that
pose a threat to human life such as natural disasters or conflicts, is of
paramount importance. The prevalence of mobile devices and the ubiquity of
network connectivity has generated a massive amount of temporally- and
spatially-stamped data. Numerous studies have used mobile data to derive
individual human mobility patterns for various applications. Similarly, the
increasing number of orbital satellites has made it easier to gather
high-resolution images capturing a snapshot of a geographical area in sub-daily
temporal frequency. We propose a novel data fusion methodology integrating
satellite imagery with privacy-enhanced mobile data to augment the event
inference task, whether in real-time or historical. In the absence of boots on
the ground, mobile data is able to give an approximation of human mobility,
proximity to one another, and the built environment. On the other hand,
satellite imagery can provide visual information on physical changes to the
built and natural environment. The expected use cases for our methodology
include small-scale disaster detection (i.e., tornadoes, wildfires, and floods)
in rural regions, search and rescue operation augmentation for lost hikers in
remote wilderness areas, and identification of active conflict areas and
population displacement in war-torn states. Our implementation is open-source
on GitHub: https://github.com/ekinugurel/SatMobFusion.
