Reliable vehicle localization remains challenging in GNSS-limited and GNSS-denied environments. This challenge becomes particularly severe under harsh Nordic winter conditions, where road markings, traffic signs, and visual landmarks are often obscured by snow. This paper presents SnowPole-GeoLoc, an open-source software framework for snow pole geo-localization using GNSS and LiDAR data fusion. In this framework, snow poles are treated as stable and machine-perceivable roadside infrastructure landmarks. The framework integrates deep learning-based snow pole detection from LiDAR-derived images with GNSS-assisted geolocalization. This combination enables the estimation of absolute pole locations in global map coordinates. The software provides modules for ROS bag processing, visualization, coordinate transformation, and quantitative evaluation against ground-truth pole locations. SnowPole-GeoLoc is evaluated using real-world data collected along Norwegian highways with a 128-channel LiDAR sensor and continuous GNSS measurements. The software is modular, reproducible, and publicly released with pretrained models, datasets, and environment specifications. It can be used as a standalone snow pole geo-localization tool or as a core sub-module within end-to-end vehicle localization pipelines designed for winter-degraded sensing conditions.