Title: Lean Images for Geo-Localization. Abstract: Most computer vision tasks use textured images. In this paper we consider the geo-localization task - finding the pose of a camera in a large 3D scene from a single lean image, i.e. an image with no texture.  We aim to experimentally explore whether texture and correlation between nearby images are necessary in a CNN-based solution for this task. Our results may give insight to the role of geometry (as opposed to textures) in a CNN-based geo-localization solution.  Lean images are projections of a simple 3D model of a city. They contain solely information that relates to the geometry of the scene viewed (edges, faces, or relative depth). We find that the network is capable of estimating the camera pose from lean images for a relatively large number of locations (order of hundreds of thousands of images). The main contributions of this paper are: (i) demonstrating the power of CNNs for recovering camera pose using lean images; and (ii) providing insight into the role of geometry in the CNN learning process;