Remote Sensing for Early Detection of Wildfires

用于早期发现野火的遥感

• 批准号: 0969032
• 负责人: Stefano Soatto
• 金额: $ 30万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-15 至 2013-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0969032&HistoricalAwards=false
• 关键词: Remote; Sensing; Early; Detection; Wildfires

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.

及早发现野火是成功应对的关键，鉴于城市化和气候变化的趋势，野火的需求日益增长。就在几个月前，在人迹罕至、无人监控的地区开始发生大规模野火，威胁大城市地区数周(天使国家森林车站大火就是例证)。随着城市扩张的边界不断扩大，地方政府的预算紧张，几十年来选择的人工观察塔已经变得不可持续。该项目正面解决了这个问题，方法是开发算法和工程工具，使用可见光和红外光谱中的联网远程光学传感器来远程探测早期火灾。虽然之前的努力建议在目标区域覆盖联网的温度和烟雾传感器，但这种方法不能很好地扩展，因为它需要传感器靠近源才能触发警报。远程传感器可以检测到远距离的事件，并且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，最终将适合与其他基础设施(如蜂窝塔)共同部署。然而，处理这些视频流并不是一件容易的事，因为感兴趣的事件，如火灾的开始，可能会以大量的方式表现出来，这取决于一天中的时间、季节、天气、到传感器的距离、燃料等。挑战是梳理这些“讨厌的影响”，并且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。