Vo Anh Vu, Bertolotto Michela, Ofterdinger Ulrich, Laefer Debra F
School of Computer Science, University College Dublin, Belfield, Dublin 4, D04 V1W8 Dublin, Ireland.
School of Natural and Built Environment, Queen's University Belfast, Stranmillis Road, Belfast, BT 95AG Northern Ireland.
Kunstliche Intell (Oldenbourg). 2023;37(1):41-53. doi: 10.1007/s13218-022-00792-4. Epub 2023 Jan 20.
Street view imagery databases such as Google Street View, Mapillary, and Karta View provide great spatial and temporal coverage for many cities globally. Those data, when coupled with appropriate computer vision algorithms, can provide an effective means to analyse aspects of the urban environment at scale. As an effort to enhance current practices in urban flood risk assessment, this project investigates a potential use of street view imagery data to identify building features that indicate buildings' vulnerability to flooding (e.g., basements and semi-basements). In particular, this paper discusses (1) building features indicating the presence of basement structures, (2) available imagery data sources capturing those features, and (3) computer vision algorithms capable of automatically detecting the features of interest. The paper also reviews existing methods for reconstructing geometry representations of the extracted features from images and potential approaches to account for data quality issues. Preliminary experiments were conducted, which confirmed the usability of the freely available Mapillary images for detecting basement railings as an example type of basement features, as well as geolocating the features.
谷歌街景、Mapillary和Karta View等街景图像数据库为全球许多城市提供了广泛的空间和时间覆盖范围。这些数据与适当的计算机视觉算法相结合，可以提供一种有效的方法来大规模分析城市环境的各个方面。作为加强当前城市洪水风险评估实践的一项工作，该项目研究了街景图像数据的一种潜在用途，即识别表明建筑物易受洪水影响的建筑特征（例如地下室和半地下室）。具体而言，本文讨论了：（1）表明存在地下室结构的建筑特征；（2）捕捉这些特征的可用图像数据源；（3）能够自动检测感兴趣特征的计算机视觉算法。本文还回顾了从图像中重建提取特征的几何表示的现有方法，以及解决数据质量问题的潜在方法。进行了初步实验，证实了免费的Mapillary图像可用于检测作为地下室特征示例类型的地下室栏杆，以及对这些特征进行地理定位。
