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In Search of Basement Indicators from Street View Imagery Data: An Investigation of Data Sources and Analysis Strategies
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 a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10239739/ https://www.ncbi.nlm.nih.gov/pubmed/37283695 http://dx.doi.org/10.1007/s13218-022-00792-4 |
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author | Vo, Anh Vu Bertolotto, Michela Ofterdinger, Ulrich Laefer, Debra F. |
author_facet | Vo, Anh Vu Bertolotto, Michela Ofterdinger, Ulrich Laefer, Debra F. |
author_sort | Vo, Anh Vu |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10239739 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-102397392023-06-06 In Search of Basement Indicators from Street View Imagery Data: An Investigation of Data Sources and Analysis Strategies Vo, Anh Vu Bertolotto, Michela Ofterdinger, Ulrich Laefer, Debra F. Kunstliche Intell (Oldenbourg) Technical Contribution 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. Springer Berlin Heidelberg 2023-01-20 2023 /pmc/articles/PMC10239739/ /pubmed/37283695 http://dx.doi.org/10.1007/s13218-022-00792-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Technical Contribution Vo, Anh Vu Bertolotto, Michela Ofterdinger, Ulrich Laefer, Debra F. In Search of Basement Indicators from Street View Imagery Data: An Investigation of Data Sources and Analysis Strategies |
title | In Search of Basement Indicators from Street View Imagery Data: An Investigation of Data Sources and Analysis Strategies |
title_full | In Search of Basement Indicators from Street View Imagery Data: An Investigation of Data Sources and Analysis Strategies |
title_fullStr | In Search of Basement Indicators from Street View Imagery Data: An Investigation of Data Sources and Analysis Strategies |
title_full_unstemmed | In Search of Basement Indicators from Street View Imagery Data: An Investigation of Data Sources and Analysis Strategies |
title_short | In Search of Basement Indicators from Street View Imagery Data: An Investigation of Data Sources and Analysis Strategies |
title_sort | in search of basement indicators from street view imagery data: an investigation of data sources and analysis strategies |
topic | Technical Contribution |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10239739/ https://www.ncbi.nlm.nih.gov/pubmed/37283695 http://dx.doi.org/10.1007/s13218-022-00792-4 |
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