[ Instrument Network Instrument Development ] In May 2019, the Royal Institute of Technology of Sweden announced the development of a new technology using satellite data and machine learning for more effective monitoring of forest fires and analysis of post-disaster damage.
In 2018, there was a serious fire in the forests of northern Sweden. Due to the use of helicopters and drones to collect optical images, GPS locations and other fire information, the efficiency and low timeliness were poor, and the forest fire prevention guidance was not effective.
The new technology of Royal Institute of Technology in Sweden is based on NASA's 24-hour free open data for infrared light sensors, radar systems Esas Sentinel-1, Sentinel-2, Landsat, Viirs and Modis satellites, through deep artificial CNV (convolutional nerves) Network) Machine learning technology to analyze the logarithm of the ratio between the images before and after the fire in the target area, and then convert the result into a binary image to distinguish between the burning area and the unburned area, so as to obtain more accurate information such as fire position and burning degree.
From 2017 to 2018, the research team of the Royal Institute of Technology in Sweden collaborated with researchers from the Department of Natural Resources and Rural Development of British Columbia, Canada, to track and analyze more than 500 forest fires. The Swedish Civil Emergency Bureau will include this in the summer as a new means of fire monitoring to further test its actual results.
(Original title: Sweden develops new technologies for monitoring forest fires)
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