  In cities with tall buildings, emergency responders need an accurate floor
level location to find 911 callers quickly. We introduce a system to estimate a
victim's floor level via their mobile device's sensor data in a two-step
process. First, we train a neural network to determine when a smartphone enters
or exits a building via GPS signal changes. Second, we use a barometer equipped
smartphone to measure the change in barometric pressure from the entrance of
the building to the victim's indoor location. Unlike impractical previous
approaches, our system is the first that does not require the use of beacons,
prior knowledge of the building infrastructure, or knowledge of user behavior.
We demonstrate real-world feasibility through 63 experiments across five
different tall buildings throughout New York City where our system predicted
the correct floor level with 100% accuracy.
