GeoVLM: Improving Automated Vehicle Geolocalisation Using Vision-Language Matching

--Cross-view geo-localisation identifies coarse geographical position of an automated vehicle by matching a ground-level image to a geo-tagged satellite image from a database. Despite the advancements in Cross-view geo-localisation, significant challenges still persist such as similar looking scenes which makes it challenging to find the correct match as the top match. Existing approaches reach high recall rates but they still fail to rank the correct image as the top match. T o address this challenge, this paper proposes GeoVLM, a novel approach which uses the zero-shot capabilities of vision language models to enable cross-view geo-localisation using interpretable cross-view language descriptions. GeoVLM is a trainable reranking approach which improves the best match accuracy of cross-view geo-localisation. GeoVLM is evaluated on standard benchmark VIGOR and University-1652 and also through real-life driving environments using Cross-View United Kingdom, a new benchmark dataset introduced in this paper . The results of the paper show that GeoVLM improves retrieval performance of cross-view geo-localisation compared to the state-of-the-art methods with the help of explainable natural language descriptions. The code is available at https://github.com/CA V-Research-Lab/GeoVLM Index T erms --cross-view geo-localisation, automated vehicles, vision-language models, satellite imagery, interpretable AI, image retrieval. OCALISA TION in automated vehicles refer to the process of finding the precise position and orientation of the automated system or a robot within a given environment relative to a chosen reference coordinate system [1]. Localisation in automated vehicles serves as a backbone for higher-level functions such as perception, planning, and control, ensuring the vehicle can navigate safely and effectively. The most common solution for estimating the geo-position of automated vehicles is Global Positioning System (GPS).