Evaluation

Geolocation Distance Evaluation

We first evaluate Ethan on its performance in making accurate geolocation predictions, with initial results presented in the table above. Ethan consistently outperforms previously established models in all distance measurement categories, showing significant improvement in accuracy within the strictest 1 km category in urban settings, achieving 27.0% accuracy, which is 2.5% higher than the closest competitor, GeoSpy. Ethan also excels in broader city, region, and country-level performance, with 55.0% accuracy at the city level (25 km), 75.5% at the region level (200 km), 91.2% at the country level (750 km), and 99.0% at the continent level (2500 km), outperforming GeoSpy at each level. Ethan exhibits an average distance accuracy of 105.0 km, surpassing GeoSpy by 5.3 km, and achieves an average score of 4600.0 in the GeoGuessr game, 4.5% higher than GeoSpy. Examples include Ethan accurately pinpointing a complex urban scene in New York City within 500 meters and predicting a rural Midwest location within 10 km, demonstrating its robustness in various settings. Conversely, GeoSpy showed slightly lower precision in these cases. Ethan's comprehensive performance enhancement underscores its advanced capabilities in interpreting complex visual data and accurately predicting geographical locations, making it a formidable tool in geolocation.

Human Competition Evaluation

Following implementation and preliminary testing, Ethan undergoes real-world evaluation through participation in the GeoGuessr game, a competitive geolocation platform. This platform serves as an ideal testing ground for assessing the practical efficacy of geolocation technologies. Ethan's performance metrics, summarized in the table above, show its robust capabilities, often surpassing human players. Ethan achieved an average score of 4550.5 compared to the human average of 4120.3, with a win rate of 85.37% versus 14.63% for human competitors. Ethan's closest distance to the correct location was 0.3 km, and its farthest was 5200.2 km, compared to 1.1 km and 5400.5 km for humans, respectively. Notable examples include accurately identifying a remote village in Norway within 2 km and an urban setting in Tokyo within 500 meters. However, Ethan faced challenges in scenarios with ambiguous geographic markers, such as misidentifying a generic beach in Australia and a desert in Nevada. These insights highlight areas for further refinement. The real-world competition validates Ethan's effectiveness and potential to transform geolocation tasks across various industries, emphasizing its advanced capabilities in interpreting complex visual data.