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Auflistung nach Autor:in "Vo, Anh Vu"

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  • Zeitschriftenartikel
    In Search of Basement Indicators from Street View Imagery Data: An Investigation of Data Sources and Analysis Strategies
    (KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Vo, Anh Vu; Bertolotto, Michela; Ofterdinger, Ulrich; Laefer, Debra F.
    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.
  • Zeitschriftenartikel
    In Search of Basement Indicators from Street View Imagery Data: An Investigation of Data Sources and Analysis Strategies
    (KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Vo, Anh Vu; Bertolotto, Michela; Ofterdinger, Ulrich; Laefer, Debra F.
    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.