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.
Bibliographical noteFunding Information:
Funding for this project was provided by the National Science Foundation as part of the project “UrbanARK: Assessment, Risk Management, & Knowledge for Coastal Flood Risk Management in Urban Areas” NSF Award 1826134, jointly funded with Science Foundation Ireland (SFI - 17/US/3450) and Northern Ireland Trust (NI - R3078NBE).
© 2023, The Author(s).
- Data quality
- Image segmentation
- Scene reconstruction
ASJC Scopus subject areas
- Artificial Intelligence