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.

Dr. Amir R. Zamir is a postdoctoral researcher at the Computer Science Department of Stanford University, CA, USA.

Dr. Asaad Hakeem is a Principal Research Scientist in the Machine Learning Division at Decisive Analytics Corporation, Arlington, VA, USA.

Dr. Luc Van Gool is a Full Professor and Head of the Computer Vision Lab at ETH Zurich, Switzerland, and the VISICS Computer Vision at KU Leuven, Belgium. His other publications include the Springer title Detection and Identification of Rare Audio-visual Cues.

Dr. Mubarak Shah is Agere Chair Professor and Director of the Center for Research in Computer Vision at the University of Central Florida, Orlando, FL, USA. He is the Series Editor of Springer’s International Series in Video Computing, and he served as an Editor-in-Chief of the Springer journal Machine Vision and Applications from 2004 to 2015.

Dr. Richard Szeliski is the Director and a founding member of the Computational Photography applied research group at Facebook, Seattle, WA, USA. He is also the author of the best-selling Springer textbook Computer Vision – Algorithms and Applications.