建筑集群的地震风险分类使用MST集群和无人机遥感
Xianteng Wang1, Xue Li1, Zhumei Liu1
1Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan 430071, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究介绍了一种新的最小跨度树 (MST) 聚类方法,用于使用空间相似性对房屋结构类型进行分类. 该方法显著提高了对各种住房结构的地震能力评估准确度.
科学领域:
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
- 结构工程 结构工程
背景情况:
- 房屋的抗震能力评估非常依赖于准确的建筑结构类型识别.
- 传统的遥感方法往往忽视空间相似性,仅依赖于个别房屋的图像特征,导致分类准确度低于最佳.
研究的目的:
- 提出和评估一个最小跨度树 (MST) 房屋聚类方法,利用空间相似性来改进房屋结构类型的分类.
- 通过完善简单,混凝土和框架房屋的分类来提高地震能力评估的准确性.
主要方法:
- 利用住宅建筑的几何特征来计算视觉距离的吉斯塔尔特系数.
- 构建了一个Delaunay三角网格,以生成房屋近距离地图,MST按视觉距离加权.
- 使用支持矢量机 (SVM) 来根据几何,纹理,高度和空间分布特征进行最终分类.
主要成果:
- MST集群方法实现了高分类准确度:95.4%的混凝土房屋和93.4%的简单房屋.
- 与基于单一家庭的方法相比,框架结构建筑的分类精度提高到87%.
- 卡帕系数增加到0.89,表明分类绩效的整体显著改善.
结论:
- 拟议的MST集群方法,结合空间相似性,大大提高了基于遥感的房屋结构类型分类的准确性.
- 这种方法为通过更精确的建筑物分类来加强地震能力评估提供了一个有希望的新方向.
- 空间相似性是一个关键因素,可以有效地用于分类建筑结构类型以减少灾害风险.
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