Remote Sensing for Early Detection of Wildfires

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Early detection of wildfires is critical to mounting a successful response, and a growing need given trends in both urbanization and climate change. As recently as a few months ago, large wildfires started in inaccessible unmonitored areas threatened large urban areas for weeks (witness the Station Fire in the Angels National Forest). Manned observation towers, the method of choice in decades past, has become unsustainable with the boundaries of urban sprawl growing and the budgets of local governments under strain.This project tackles the problem head-on by developing algorithmic and engineering tools for remote detection of incipient fires using networked remote optical sensors in the visible and infra-red spectra. While previous efforts suggested blanketing the target area with networked temperature and smoke sensors, this approach does not scale well because it requires sensors to be close to the source in order to trigger an alarm. Remote sensors can detect events at a distance and are only limited by the topography of the environment. Thus one strategically placed camera can monitor an entire valley and would ultimately be suitable for co-deployment with other infrastructure such as cell towers. However, processing these video streams is not trivial since events of interest, such as the inception of a fire, can manifest themselves in a large number of ways depending on time of the day, season, weather, distance from the sensor, fuel etc. The challenge is to tease apart these "nuisance effects" and only detect events of interest. The team will focus on the algorithmic challenge of inferring spatio-temporal events in video streams, and on the systems trade-offs between computation, communication and energy resources.