  The concept of geo-localization refers to the process of determining where on
earth some `entity' is located, typically using Global Positioning System (GPS)
coordinates. The entity of interest may be an image, sequence of images, a
video, satellite image, or even objects visible within the image. As massive
datasets of GPS tagged media have rapidly become available due to smartphones
and the internet, and deep learning has risen to enhance the performance
capabilities of machine learning models, the fields of visual and object
geo-localization have emerged due to its significant impact on a wide range of
applications such as augmented reality, robotics, self-driving vehicles, road
maintenance, and 3D reconstruction. This paper provides a comprehensive survey
of geo-localization involving images, which involves either determining from
where an image has been captured (Image geo-localization) or geo-locating
objects within an image (Object geo-localization). We will provide an in-depth
study, including a summary of popular algorithms, a description of proposed
datasets, and an analysis of performance results to illustrate the current
state of each field.
