Accurate seismic risk modeling requires knowledge of key structural characteristics of buildings. However, to date, the collection of such data is highly expensive in terms of labor, time and money and thus prohibitive for a spatially continuous large-area monitoring. This study quantitatively evaluates the potential of an automated and thus more efficient collection of vulnerability-related structural building characteristics based on Deep Convolutional Neural Networks (DCNNs) and street-level imagery such as provided by Google Street View. The proposed approach involves a tailored hierarchical categorization workflow to structure the highly heterogeneous street-level imagery in an application-oriented fashion. Thereupon, we use state-of-the-art DCNNs to explore the automated inference of Seismic Building Structural Types. These reflect the main-load bearing structure of a building, and thus its resistance to seismic forces. Additionally, we assess the independent retrieval of two key building structural parameters, i.e., the material of the lateral-load-resisting system and building height to investigate the applicability for a more generic structural characterization of buildings. Experimental results obtained for the earthquake-prone Chilean capital Santiago show accuracies beyond κ = 0.81 for all addressed classification tasks. This underlines the potential of the proposed methodology for an efficient in-situ data collection on large spatial scales with the purpose of risk assessments related to earthquakes, but also other natural hazards (e.g., tsunamis, or floods).

准确的地震风险建模需要了解建筑物的关键结构特征。然而，迄今为止，收集此类数据在劳动力、时间和金钱方面非常昂贵，因此无法进行空间连续的大面积监测。本研究基于深度卷积神经网络 (DCNN) 和街道级图像（例如由 Google 街景提供），定量评估了自动化并因此更有效地收集与漏洞相关的结构建筑特征的潜力。所提出的方法涉及定制的分层分类工作流，以面向应用程序的方式构建高度异构的街道级图像。因此，我们使用最先进的 DCNN 来探索地震建筑结构类型的自动推理。这些反映了建筑物的主要承重结构，从而反映了其对地震力的抵抗力。此外，我们评估了两个关键建筑结构参数的独立检索，即抗侧向荷载系统的材料和建筑高度，以研究更通用的建筑结构特征的适用性。在地震多发的智利首都圣地亚哥获得的实验结果表明，所有分类任务的准确度都超过了 κ = 0.81。这突显了拟议方法的潜力 侧向荷载系统的材料和建筑高度，以研究更通用的建筑结构特征的适用性。在地震多发的智利首都圣地亚哥获得的实验结果表明，所有分类任务的准确度都超过了 κ = 0.81。这突显了拟议方法的潜力 侧向荷载系统的材料和建筑高度，以研究更通用的建筑结构特征的适用性。在地震多发的智利首都圣地亚哥获得的实验结果表明，所有分类任务的准确度都超过了 κ = 0.81。这突显了拟议方法的潜力大空间尺度的原位数据收集，目的是评估与地震有关的风险，但也包括其他自然灾害（例如海啸或洪水）。