research-article

Joining Street-View Images and Building Footprint GIS Data

Authors:

Shenglong Chen,

Yoshihide SekimotoAuthors Info & Claims

GeoSearch'21: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data

Pages 18 - 24

https://doi.org/10.1145/3486640.3491395

Published: 08 November 2021 Publication History

Abstract

This paper proposes a new method to join building footprint GIS data with the relevant buildings in a street-view image, taken by a vehicle-mounted camera. This is achieved by segmenting buildings in the street-view images and identifying the relevant building coordinates in the image. The building coordinates on the image are then estimated from the building vertices in the building footprint GIS data and vehicle trajectory history. Finally, the objective building is identified and relevant building attributes corresponding to each building image are linked together. This method enables the development of building image datasets with associated building attributes. The building image data, when linked to the relevant building attributes, could contribute to many innovative urban analyses, such as urban monitoring, the development of three-dimensional (3D) city models, and image datasets for training with annotated building attributes.

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Cited By

Ogawa YNakamura RSato GMaeda HSekimoto Y(2024)End-to-End Framework for the Automatic Matching of Omnidirectional Street Images and Building Data and the Creation of 3D Building ModelsRemote Sensing10.3390/rs1611185816:11(1858)Online publication date: 23-May-2024
https://doi.org/10.3390/rs16111858
Roussel RJacoby SAsadipour A(2024)Robust Building Identification from Street Views Using Deep Convolutional Neural NetworksBuildings10.3390/buildings1403057814:3(578)Online publication date: 21-Feb-2024
https://doi.org/10.3390/buildings14030578
Wu CYu XMa CZhong RZhou X(2024) Integrating geospatial data and street‐view imagery to reconstruct large‐scale 3D urban building models Transactions in GIS10.1111/tgis.1319228:5(1326-1352)Online publication date: 4-Jun-2024
https://doi.org/10.1111/tgis.13192
Show More Cited By

Index Terms

Joining Street-View Images and Building Footprint GIS Data
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems

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cover image ACM Conferences

GeoSearch'21: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data

November 2021

30 pages

ISBN:9781450391238

DOI:10.1145/3486640

Editors:
Gabriele Cavallaro
Forschungszentrum Jülich, Germany
,
Dora B. Heras
University of Santiago de Compostela, Spain
,
Dalton Lunga
Oak Ridge National Lab, TN, USA
,
Martin Werner
Technical University Munich (TUM), Germany
,
Andreas Züfle
George Mason University, VA, USA

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 November 2021

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SIGSPATIAL '21

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SIGSPATIAL

SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems

November 2, 2021

Beijing, China

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Cited By

Ogawa YNakamura RSato GMaeda HSekimoto Y(2024)End-to-End Framework for the Automatic Matching of Omnidirectional Street Images and Building Data and the Creation of 3D Building ModelsRemote Sensing10.3390/rs1611185816:11(1858)Online publication date: 23-May-2024
https://doi.org/10.3390/rs16111858
Roussel RJacoby SAsadipour A(2024)Robust Building Identification from Street Views Using Deep Convolutional Neural NetworksBuildings10.3390/buildings1403057814:3(578)Online publication date: 21-Feb-2024
https://doi.org/10.3390/buildings14030578
Wu CYu XMa CZhong RZhou X(2024) Integrating geospatial data and street‐view imagery to reconstruct large‐scale 3D urban building models Transactions in GIS10.1111/tgis.1319228:5(1326-1352)Online publication date: 4-Jun-2024
https://doi.org/10.1111/tgis.13192
Rydlewski JRajabi ZTariq MMuttil NSidiqui PShah AKhan NIrshad MAlam AButt TNg A(2022)Identification of Embodied Environmental Attributes of Construction in Metropolitan and Growth Region of Melbourne, Australia to Support Urban PlanningSustainability10.3390/su1414840114:14(8401)Online publication date: 8-Jul-2022
https://doi.org/10.3390/su14148401

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