In this letter, we present a long-term localization method that effectively exploits the structural information of an environment via an image format. The proposed method presents a robust year-round localization performance even when learned in just a single day. The proposed localizer learns a point cloud descriptor, named Scan Context Image (SCI), and performs robot localization on a grid map by formulating the place recognition problem as place classification using a convolutional neural network. Our method is faster than existing methods proposed for place recognition because it avoids a pairwise comparison between a query and scans in a database. In addition, we provide thorough validations using publicly available long-term datasets, the NCLT dataset and the Oxford RobotCar dataset, and show that the Scan Context Image (SCI) localization attains consistent performance over a year and outperforms existing methods.