Developing a data analytics tool for real-time hotspot detection and geolocalization

MVT GoSolutions是加拿大领先的基于无人机开发地理空间技术的公司。他们最近加入了国家和省级更有效地管理野火和森林火灾的努力。传统上，对野火的探测和监视主要是通过结合被称为瞭望塔的固定观测网络、空中探测巡逻和卫星图像来完成的。如今的瞭望塔配备了物联网传感器，可以持续监测环境；然而，它们是逐点的，不能覆盖大面积。卫星图像是一种重要的野火管理手段，可以覆盖大片土地，识别中大型火灾。然而，卫星访问周期往往太不频繁，图像分辨率太低，无法及时发现小的、正在发展的热点。飞机可以以更高的频率部署，以收集优先区域的感官观察。与有人驾驶飞机相比，无人驾驶飞机可以在不危及操作员生命的情况下达到较低的飞行高度，可以飞到难以到达的地区，也可以根据需要频繁使用。技术进步和小型高效传感器的可负担性提高，使无人机（UAV）能够收集小型火灾或热点的高分辨率数据。在这方面，CGQ将协助MVT开发一种工具，该工具可以通过应用先进的地理空间分析，从配备测距、视觉、热、惯性和定位传感器的无人机捕获的数据中检测和定位热点。

MVT GoSolutions is a Canadian leader in developing geospatial technologies based on drones. They have recently joined the national and provincial efforts to manage wildfires and forest fires more efficiently. Traditionally, the detection and surveillance of wildland fires have mainly been performed by combining stationary observation networks known as watchtowers, aerial detection patrols, and satellite imagery. Watchtowers are nowadays equipped with IoT sensors that can continuously monitor the environment; however, they are point-wise and cannot cover a large area. Satellite imagery is a significant means of wildfire management covering large swaths of land and identifying medium to large fires. However, satellite visit periods are often too infrequent, and image resolution is too low to promptly detect small, developing hotspots. Aircraft can be deployed at higher frequencies to gather sensory observations of priority areas. Compared to manned aircraft, unmanned aircraft can reach a lower flight altitude without endangering operators' lives, they can fly to hard-to-reach areas, and they can also be used as frequently as needed. Technical advances and improved affordability of small yet efficient sensors empower unmanned aerial vehicles (UAV) with the capacity to gather high-resolution data of small fires or hotspots. With this regard, CGQ will assist MVT in developing a tool that can detect and geo-localize hotspots from the data captured by a UAV equipped with ranging, visual, thermal, inertial, and positioning sensors via applying advanced geospatial analytics.