基于边缘计算的优化林火探测卫星星座
An Optimised Satellite Constellation for Forest Fire Detection through Edge Computing
摘要：
2019年底，澳大利亚发生了一场大火危机，影响了超过10万平方公里的土地，摧毁了2000多所房屋。在这里，我们提出了一种高效率的在轨火灾探测方法，以防止这种灾难的重演。首先通过NSGA-II（非支配排序遗传算法II）开发LEO卫星星座，优化澳大利亚的覆盖范围。然后采用边缘计算的方法，利用多颗星座卫星作为边缘节点，运行一种多目标火灾探测算法，以减少火灾探测时间。对地静止卫星用于卫星间通信，使得由卫星拍摄的图像可以分布在若干卫星之间以进行处理。地球静止卫星还保持与地面的持续联系，因此可以在没有任何重大延迟的情况下报告火灾探测结果。总的来说，该系统能够检测长度超过5米的火灾，并且可以在1.39秒内处理每个图像进行检测。这比目前任何可用的火灾探测方法都要快。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
