Remote Sensing for Early Detection of Wildfires

及早发现野火对于成功应对至关重要，鉴于城市化和气候变化的趋势，这一需求日益增长。就在几个月前，大型野火在无法进入的未受监控的地区开始，威胁着大城市地区长达数周（见证了天使国家森林的车站火灾）。随着城市扩张的边界不断扩大和地方政府预算紧张，过去几十年的首选方法——载人瞭望塔已经变得不可持续。该项目通过开发算法和工程工具，在可见光和红外光谱中使用联网的远程光学传感器来远程探测早期火灾，从而正面解决了这个问题。虽然之前的努力建议用联网的温度和烟雾传感器覆盖目标区域，但这种方法并不适用，因为它需要传感器靠近信号源才能触发警报。遥测器可以探测远处的事件，只受环境地形的限制。因此，一个战略性放置的摄像头可以监控整个山谷，并最终适合与其他基础设施（如手机信号塔）共同部署。然而，处理这些视频流并不是微不足道的，因为感兴趣的事件，如火灾的开始，可以以多种方式表现出来，这取决于一天中的时间、季节、天气、与传感器的距离、燃料等。挑战在于梳理这些“讨厌的影响”，只检测感兴趣的事件。该团队将专注于在视频流中推断时空事件的算法挑战，以及计算、通信和能源资源之间的系统权衡。

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.