Towards Diverse Global Image Geolocalization: Baselines, Dataset, and Benchmark

We introduce Diverse Global Image Geolocalization, an extended task that generalizes image geolocalization beyond the street-view domain to ten distinct domains. Inputs mainly originate from continuous domains, such as street-view, remote sensing, terrain and plant, and maps, or from discrete domains, such as landmark, road-network, indoor, shapes, space, and UAV imagery. We provide a complete suite for this task, comprising GeoVerse100K, a training dataset, a ten-domain evaluation benchmark, and a baseline. GeoVerse100K is constructed with a geographic-attribute sampling method to ensure balanced geographic coverage and a reward-based sampling method to better support RL-based training. We further divide the benchmark into 37 subcategories and conduct a comprehensive evaluation of both general and specialized models. To establish a strong baseline, we propose two training strategies tailored to the multi-domain setting: domain-balanced sampling and a difficulty-guided curriculum. We then finetune Qwen3.5-9B using GRPO with a distance-aware reward. Experiments show that current specialized models suffer from severe generalization limitations, whereas our baseline trained on GeoVerse100K achieves balanced performance across all ten domains. The code and dataset will be made publicly available.