论文标题
通过边缘计算的优化卫星星座用于森林火灾检测
An Optimised Satellite Constellation for Forest Fire Detection through Edge Computing
论文作者
论文摘要
2019年底标志着澳大利亚的丛林大火危机影响了100000公里以上的土地，并摧毁了2000多座房屋。在这里，我们提出了一种具有高效率的轨内丛林检测方法，以防止重复这场灾难。 LEO卫星星座首先是通过NSGA-II（非主导分类遗传算法II）开发的，对澳大利亚覆盖范围进行了优化。然后采用边缘计算以使用几个星座卫星作为边缘节点来运行丛林检测算法，以减少火灾检测时间。地理卫星用于卫星间通信，以便可以将卫星拍摄的图像分布在几个卫星中进行处理。地静止的卫星还保持与地面的持续联系，因此可以在没有任何明显延迟的情况下进行灌木丛检测。总体而言，该系统能够检测到长度超过5m的火灾，并且可以在每张图像中以1.39s进行检测。这比当前可用的丛林检测方法快。
The end of 2019 marked a bushfire crisis for Australia that affected more than 100000km2 of land and destroyed more than 2000 houses. Here, we propose a method of in-orbit bushfire detection with high efficiency to prevent a repetition of this disaster. An LEO satellite constellation is first developed through NSGA-II (Nondominated Sorting Genetic Algorithm II), optimising for coverage over Australia. Then edge computing is adopted to run a bushfire detection algorithm using several constellation satellites as edge nodes to reduce fire detection time. A geostationary satellite is used for inter-satellite communications, such that an image taken by a satellite can be distributed among several satellites for processing. The geostationary satellite also maintains a constant link to the ground, so that a bushfire detection can be reported back without any significant delay. Overall, this system is able to detect fires that span more than 5m in length, and can make detections in 1.39s per image processed. This is faster than any currently available bushfire detection method.
