Large-Scale Visual Geo-Localization

  • Amir R. Zamir
  • Asaad Hakeem
  • Luc Van Gool
  • Mubarak Shah
  • Richard Szeliski

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

Table of contents

  1. Front Matter
    Pages i-xi
  2. Amir R. Zamir, Asaad Hakeem, Luc Van Gool, Mubarak Shah, Richard Szeliski
    Pages 1-18
  3. Data-Driven Geo-localization

    1. Front Matter
      Pages 19-19
    2. Yong Jae Lee, Alexei A. Efros, Martial Hebert
      Pages 21-40
    3. Li-Jia Li, Rahul Kumar Jha, Bart Thomee, David Ayman Shamma, Liangliang Cao, Yang Wang
      Pages 41-58
    4. Tsung-Yi Lin, Serge Belongie, James Hays
      Pages 59-76
    5. Mayank Bansal, Kostas Daniilidis, Harpreet Sawhney
      Pages 77-98
  4. Semantic Reasoning Based Geo-localization

    1. Front Matter
      Pages 99-99
    2. Gautam Singh, Jana Košecká
      Pages 101-120
    3. David J. Crandall, Yunpeng Li, Stefan Lee, Daniel P. Huttenlocher
      Pages 121-144
  5. Geometric Matching Based Geo-localization

    1. Front Matter
      Pages 145-145
    2. Yunpeng Li, Noah Snavely, Daniel P. Huttenlocher, Pascal Fua
      Pages 147-163
    3. Torsten Sattler, Bastian Leibe, Leif Kobbelt
      Pages 165-187
    4. Hyun Soo Park, Yu Wang, Eriko Nurvitadhi, James C. Hoe, Yaser Sheikh, Mei Chen
      Pages 189-203
    5. Olivier Saurer, Georges Baatz, Kevin Köser, L’ubor Ladický, Marc Pollefeys
      Pages 205-223
    6. Jiejie Zhu, Mayank Bansal, Nick Vander Valk, Hui Cheng
      Pages 225-237
    7. Eric Tzeng, Andrew Zhai, Matthew Clements, Raphael Townshend, Avideh Zakhor
      Pages 239-254
    8. Mathieu Aubry, Bryan Russell, Josef Sivic
      Pages 255-275
  6. Real-World Applications

    1. Front Matter
      Pages 277-277
    2. Sudipta N. Sinha, Varsha Hedau, C. Lawrence Zitnick, Richard Szeliski
      Pages 279-298

About this book

Introduction

This timely and authoritative volume explores the bidirectional relationship between images and locations. The text presents a comprehensive review of the state of the art in large-scale visual geo-localization, and discusses the emerging trends in this area. Valuable insights are supplied by a pre-eminent selection of experts in the field, into a varied range of real-world applications of geo-localization.

Topics and features:

  • Discusses the latest methods to exploit internet-scale image databases for devising geographically rich features and geo-localizing query images at different scales
  • Investigates geo-localization techniques that are built upon high-level and semantic cues
  • Describes methods that perform precise localization by geometrically aligning the query image against a 3D model
  • Reviews techniques that accomplish image understanding assisted by the geo-location, as well as several approaches for geo-localization under practical, real-world settings
  • Presents contributions from the leading and most active researchers in the field from both academia and industry

This invaluable text/reference is a must-read for all researchers interested in developing automatic methods for image geo-localization, whether for commercial, academic, or military domains. Professionals involved in computer vision, computer graphics, photogrammetry, computational optimization, geographic information systems, and other related disciplines, will also benefit from the detailed coverage of this emerging field.

Keywords

Context-based Reasoning of GIS and Geo-Reference Data Data-Driven Discovery of Geo-Informative Features Geolocalization and Mapping Geometric Modeling and Reasoning Visual Indexing and Retrieval of Large-Scale Imagery

Editors and affiliations

  • Amir R. Zamir
    • 1
  • Asaad Hakeem
    • 2
  • Luc Van Gool
    • 3
  • Mubarak Shah
    • 4
  • Richard Szeliski
    • 5
  1. 1.Computer Science DepartmentStanford University Computer Science DepartmentStanfordUSA
  2. 2.Decisive Analytics CorporationArlingtonUSA
  3. 3.ETH ZürichZürichSwitzerland
  4. 4.University of Central FloridaOrlandoUSA
  5. 5.FacebookSeattleUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-25781-5
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-25779-2
  • Online ISBN 978-3-319-25781-5
  • Series Print ISSN 2191-6586
  • Series Online ISSN 2191-6594
  • About this book