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使用高分辨率图像对灾难恢复进行建筑屋顶损坏的分类

Elaina Gonsoroski¹, Yoonjung Ahn², Emily W Harville³

¹Department of Geography, College of Social Sciences and Public Policy, Florida State University, Tallahassee, FL 32306.

Photogrammetric engineering and remote sensing

|March 15, 2024

概括

风后的损害评估可以使用空中图像来简化,以检测蓝色布,这是建筑损坏和像迈克尔风这样的大风暴后的恢复需求的关键指标.

科学领域:

遥感遥感遥感遥感
灾害管理灾害管理
地理空间分析的研究.

背景情况:

传统的风后损害评估需要大量的资源和时间.
远程传感数据为数据收集提供了一个快速且具有成本效益的替代方案.
风迈克尔 (2018年) 在佛罗里达州15个县造成了重大破坏.

研究的目的:

评估空中图像的成本效益,以测量灾后建筑物上的蓝色帆布.
评估支持矢量机器模型的实用性,用于识别蓝色帆布和指示建筑损坏.
为资源有限的司法管辖区提供可扩展的损害评估方法.

主要方法:

利用了来自佛罗里达州15个受风迈克尔影响的县的空中图像.
开发并应用了支矢量机器模型来对建筑地块上的蓝色地毯进行分类.
根据模型的蓝色天花板检测,为包裹分配了损坏指示器.

主要成果:

支持矢量机器模型的整体精度为85.3% (74%的灵敏度,96.7%的特异性).
在研究区域中,约有7%的土地 (共32357个) 被确定为蓝色帆布.
检测到的蓝色帆布标志着需要灾难影响和恢复关注的地区.

结论:

空中图像与机器学习相结合,为风后的损害评估提供了一种具有成本效益的方法.
这种方法可以大大帮助那些资金有限的司法管辖区进行实地评估.
检测蓝色布可以作为灾难影响和恢复监测的有价值的指标.

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