| 青海省地震灾后地物变化的快速识别 |
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| 引用本文： | 韩建平.青海省地震灾后地物变化的快速识别[J].测绘通报,2021,0(11):120-123. |
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| 作者姓名： | 韩建平 |
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| 作者单位： | 青海地理信息产业发展有限公司, 青海 西宁 810001 |
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| 摘 要： | 地震灾后救援时间宝贵，有效利用灾后数据，快速对救援路线及居民情况进行准确的评估，能有效避免地震带来的进一步损害。地震并不是一次即停止，灾后余震同样威胁灾区群众的生命和财产安全。因此，根据灾区地质及居民分布制定后续可持续救援方案，也是灾后救援的重点，这不仅能使救援稳步进行，也可进一步规划安置居民。本文通过比较神经网络和传统机器学习方法，提出了自适应方法的地物分割算法，根据航拍影像的复杂程度使用神经网络和传统算法相结合的方式，通过比较识别出地物与原有测量数据的差别变化，判断灾后道路扭曲和地质变化，为救援工作提供准确的科学依据。
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| 关 键 词： | 地震 救援 灾后 青海 神经网络 变化检测 |
| 收稿时间： | 2021-08-05 |
The rapid identification of ground features after the earthquake disaster in Qinghai province |
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| HAN Jianping.The rapid identification of ground features after the earthquake disaster in Qinghai province[J].Bulletin of Surveying and Mapping,2021,0(11):120-123. |
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| Authors: | HAN Jianping |
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| Institution: | Qinghai Geographic Information Industry Development Co., Ltd., Xining 810001, China |
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| Abstract: | Every minute of post-earthquake rescue is precious. Effective use of post-disaster data and accurate assessment of rescue routes can effectively avoid further damage caused by the earthquake. The earthquake did not stop at once, and the aftershocks after the disaster also threaten the lives and property safety of the people in the disaster area. Therefore, formulating a follow-up sustainable rescue plan based on the geology of the disaster area and the distribution of residents is the top priority of post-disaster rescue, which not only enables the rescue to be carried out steadily, but also allows for further planning of residential resettlement. This paper proposes an adaptive method of ground object segmentation algorithm by comparing neural network and traditional machine learning methods. According to the complexity of aerial imagery, neural network and traditional algorithm are combined to compare the identified ground objects with the original measurement data. Differential changes can be used to judge road distortions and geological changes after the disaster and provide an accurate scientific basis for rescue work. |
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| Keywords: | earthquake rescue after disaster Qinghai neural networks change detection |
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