Settlements induced by tunneling in inner urban areas can easily damage above ground structures. This already has to be considered in early planning of tunneling routes. Assessing the risk of damages to structures on hypothetical tunneling routes inflicted by such settlements beforehand enables routes’ comparability. Hereby, it facilitates the choice of the optimal tunneling route in terms of potential damages and of suitable countermeasures. Risk analyses of structures establishing the assessment obtain relevant data from various sources. Some data even has to be gathered manually. Virtual building models could ease this process and facilitate analyses for entire districts as they combine several required information in a single data set. Commonly, these are yet modelled very coarse. Relevant details like facade openings, which highly affect a structures stiffness, are not included. In this paper, we propose a system which detects windows in facade images. This is used to subsequently enrich existing virtual building models allowing for a precise risk assessment. For this, we apply a sliding window detector which employs a cascaded classifier to obtain windows in images patches. Our system yields sufficient results on facade images of several countries showing its general applicability despite regional and architectural variation in the facades’ and windows’ appearance. In an ensuing case study, we assess the risk of damages to structures based on detections of our system using different analysis methods. We contrast these results to assessments using manually gathered data. Hereby, we show that the detection rate of our proposed system is sufficient for a reliable estimation of a structure’s damage class.

中文翻译：

---

在立面图像中进行窗口检测以进行隧道风险评估

在市区内部，隧道开挖引起的居民定居点很容易破坏地上结构。在隧道路线的早期规划中已经必须考虑到这一点。事先评估由此类沉降造成的假设隧道路线上的结构损坏的风险，可以使路线具有可比性。因此，根据潜在的损坏和适当的对策，它有助于选择最佳的隧道路线。建立评估的结构的风险分析可从各种来源获得相关数据。有些数据甚至必须手动收集。虚拟建筑模型可以简化此过程，并有助于对整个区域进行分析，因为它们将多个必需的信息组合到一个数据集中。通常，这些模型的建模非常粗糙。相关细节，例如立面开口，高度影响结构刚度的因素不包括在内。在本文中，我们提出了一种检测立面图像中的窗口的系统。这用于随后丰富现有的虚拟建筑模型，以进行精确的风险评估。为此，我们应用了滑动窗口检测器，该检测器采用级联分类器来获取图像块中的窗口。尽管在外观和窗户外观方面存在区域和建筑差异，但我们的系统在多个国家/地区的立面图像上都能显示出足够的结果，表明其总体适用性。在随后的案例研究中，我们基于使用不同分析方法对系统的检测来评估结构损坏的风险。我们将这些结果与使用手动收集的数据进行的评估进行对比。特此，