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Current technologies for localization such as Global Positioning Systems (GPS) and Inertial Measurement Unit (IMU) have limitations in terms of accuracy, cost, signal attenuation and need for re-calibration over time, which leads to a compromise in performance and accuracy. In this paper, we present a novel Artificial Landmark-Based Identification System (ALIS), which can be used in both indoor and outdoor localization applications. ALIS is a multi-part landmark-based localization approach, which uses template matching techniques to find multiple landmark patterns in an image to estimate distance from the landmark. A novel combination of pre- and post-processing techniques is used to improve template matching results. The image is enhanced by using high boost filtering and histogram equalization techniques, which sharpen the feature edges and improves the image contrast, respectively. Instead of simple averaging techniques, a novel dynamic exclusion heuristic is utilized, which converges the distance estimation results closer to the actual physical distance. Significant contributions of this paper are in the use of server assistance in pattern identification, Graphic Processing Unit (GPU) for faster parallel processing of computationally intensive algorithms, and multiple pattern identification for better distance estimation.