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Introduction to Large-Scale Visual Geo-localization

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Large-Scale Visual Geo-Localization

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Despite recent advances in computer vision and large-scale indexing techniques, automatic geo-localization of images and videos remains a challenging task. The majority of existing computer vision solutions for geo-localization are limited to highly-visited urban regions for which a significant amount of geo-tagged imagery is available, and therefore, do not scale well to large and ordinary geo-spatial regions. In this chapter, we provide an overview of the major research themes in visual geo-localization, investigate the challenges, and point to problem areas that will benefit from common synthesis of perspectives from these research themes. In particular, we discuss how the availability of web-scale geo-referenced data affects visual geo-localization, what role semantic information plays in this problem, and how precise localization can be achieved using large-scale textured (RGB) and untextured (non-RGB) 3D models. We also introduce a few real-world applications which became feasible as a result of the capability of estimating an image’s geo-location. We conclude this chapter by providing an overview of the emerging trends in visual geo-localization and a summary of the rest of the chapters of the book.

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Author information

Authors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Amir R. Zamir

  2. Decisive Analytics Corporation, Arlington, VA, USA

    Asaad Hakeem

  3. ETH Zurich, Zurich, Switzerland

    Luc Van Gool

  4. University of Central Florida, Orlando, FL, USA

    Mubarak Shah

  5. Facebook, Seattle, WA, USA

    Richard Szeliski

Authors
  1. Amir R. Zamir
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  2. Asaad Hakeem
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  3. Luc Van Gool
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  4. Mubarak Shah
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  5. Richard Szeliski
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Corresponding author

Correspondence to Amir R. Zamir .

Editor information

Editors and Affiliations

  1. Computer Science Department, Stanford University Computer Science Department, Stanford, California, USA

    Amir R. Zamir

  2. Decisive Analytics Corporation, Arlington, Virginia, USA

    Asaad Hakeem

  3. ETH Zürich, Zürich, Switzerland

    Luc Van Gool

  4. University of Central Florida, Orlando, Florida, USA

    Mubarak Shah

  5. Facebook, Seattle, Washington, USA

    Richard Szeliski

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© 2016 Springer International Publishing Switzerland

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Zamir, A.R., Hakeem, A., Van Gool, L., Shah, M., Szeliski, R. (2016). Introduction to Large-Scale Visual Geo-localization. In: Zamir, A., Hakeem, A., Van Gool, L., Shah, M., Szeliski, R. (eds) Large-Scale Visual Geo-Localization. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-25781-5_1

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  • DOI: https://doi.org/10.1007/978-3-319-25781-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25779-2

  • Online ISBN: 978-3-319-25781-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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