  Global physical event detection has traditionally relied on dense coverage of
physical sensors around the world; while this is an expensive undertaking,
there have not been alternatives until recently. The ubiquity of social
networks and human sensors in the field provides a tremendous amount of
real-time, live data about true physical events from around the world. However,
while such human sensor data have been exploited for retrospective large-scale
event detection, such as hurricanes or earthquakes, they has been limited to no
success in exploiting this rich resource for general physical event detection.
  Prior implementation approaches have suffered from the concept drift
phenomenon, where real-world data exhibits constant, unknown, unbounded changes
in its data distribution, making static machine learning models ineffective in
the long term. We propose and implement an end-to-end collaborative drift
adaptive system that integrates corroborative and probabilistic sources to
deliver real-time predictions. Furthermore, out system is adaptive to concept
drift and performs automated continuous learning to maintain high performance.
We demonstrate our approach in a real-time demo available online for landslide
disaster detection, with extensibility to other real-world physical events such
as flooding, wildfires, hurricanes, and earthquakes.
