面向地震巨灾保险的建筑特性快速提取方法

房屋建筑分类是抗震设计和地震风险分析的基础，是巨灾保险的纽带环节，也是结构易损性准确、完备分析的前驱保障，快速获取建筑特性参数非常关键。基于影像数据获取结构特性相比传统手段具有显著优势，然而其准确性具有一定挑战性，从影像数据得到实时的、较准确的结构特性成为地震保险数据获取技术的关注焦点。本文采用深度学习方法开展从影像数据中提取面向地震保险需求的建筑特性数据，构建基于深度学习方法的建筑高度识别模型和基于机器视觉的建筑高度识别方法，运用基于Xception神经网络深度学习和机器视觉的模型，对北京地区的建筑高度进行模型测试，该方法可为地震保险分析提供重要的基础数据支持。

Fast Extraction Method of Building Characteristics for Earthquake Catastrophe Insurance

Building classification is the basis of seismic design and earthquake risk analysis, and is also the link of catastrophe insurance. It is also the precursor to the accurate and complete analysis of structural vulnerability, and it is critical to quickly obtain building characteristic parameters. Obtaining structural characteristics based on image data has significant advantages over traditional methods. However, the accuracy of its related methods is a very challenging problem. Obtaining more accurate structural characteristics from image data in real time has become the focus of seismic insurance data acquisition technology. In this paper, deep learning method is used to extract building characteristic data oriented to earthquake insurance from image data. Building height recognition model based on deep learning methods were performed and applied in Beijing. The method can provide important basic data support for earthquake insurance analysis.