Cross-View Geo-Localisation

An interactive, first-person, partially-observed visual environment that uses Google Street View for its photographic content and broad coverage, and give performance baselines for a challenging goal-driven navigation task. Source: The StreetLearn Environment and Dataset

Similar to CVUSA and CVACT, the VIGOR dataset contains satellites and street imagery to match them to each other to find the location of the street imagery. For this purpose, data from 4 major American cities were used, namely San Francisco, New York, Seattle and Chicago. Unlike the previous datasets, there are two settings: The SAME-Area setting where images of all cities are available in training and validation split. Secondly, there is the CROSS area setting where training is done on two cities (New York, Seattle) and evaluation is done on Chicago and San Francisco. In addition, the dataset contains semi-positive images which are very close to an actual ground truth image and thus serve as a distraction for the matching task. In total, the dataset consists of 90,618 satellite images and 105,214 street images.

Cross-View Geo-Localisation within urban regions is challenging in part due to the lack of geo-spatial structuring within current datasets and techniques. We propose utilising graph representations to model sequences of local observations and the connectivity of the target location. Modelling as a graph enables generating previously unseen sequences by sampling with new parameter configurations. SpaGBOL contains 98,855 panoramic streetview images across different seasons, and 19,771 corresponding satellite images from 10 mostly densely populated international cities. This translates to 5 panoramic images and one satellite image per graph node. Downloading instructions below.

This dataset builds upon the SpaGBOL dataset - a graph-based dataset covering numerous cities across the globe for the purpose of structured city-scale Cross-View Geo-Localisation (CVGL). We introduce the idea of Multi-View Geo-Localisation (MVGL) - localising in two steps within a graph. First using CVGL to retrieve the most likely primary nodes (road junctions), before performing relative pose estimation also candidate edges to increase precision. The corresponding initial network proposed reduced median error from hundreds of metres down to approximately 22m error across the entire dataset.