This study proposes a novel method to assess damages in the built environment using a deep learning workflow to quantify it. Thanks to an automated crawler, aerial images from before and after a natural disaster of 50 epicenters worldwide were obtained from Google Earth, generating a 10,000 aerial image database with a spatial resolution of 2 m per pixel. The study utilizes the algorithm Seg-Net to perform semantic segmentation of the built environment from the satellite images in both instances (prior and post-natural disasters). For image segmentation, Seg-Net is one of the most popular and general CNN architectures. The Seg-Net algorithm used reached an accuracy of 92% in the segmentation. After the segmentation, we compared the disparity between both cases represented as a percentage of change. Such coefficient of change represents the damage numerically an urban environment had to quantify the overall damage in the built environment. Such an index can give the government an estimate of the number of affected households and perhaps the extent of housing damage.
翻译:这项研究提出一种新的方法来评估建筑环境的损害,利用深层学习工作流程来量化。由于从谷歌地球获得了自动爬行器,从全球50个中心发生自然灾害前后的空中图像,从谷歌地球获得了10 000个航空图像数据库,空间分辨率为每像素2米。该研究利用Seg-Net算法对建筑环境进行两处卫星图像(先天和后自然灾害)的语义分割。关于图像分割,Seg-Net是最受欢迎和最普遍的CNN结构之一。Seg-Net算法在分割中达到92%的精确度。在分割后,我们比较了两种情况之间的差别,即变化的百分比。这种变化系数代表了城市环境在数字上对建筑环境的总体损害进行量化。这种指数可以向政府提供受影响家庭的数量估计,或许是住房损害的程度。