Game4Loc: A UAV Geo-Localization Benchmark from Game Data
Authors: name: Yuxiang Ji, name: Boyong He, name: Zhuoyue Tan, name: Liaoni Wu

The vision-based geo-localization technology for UAV, serving as a secondary
source of GPS information in addition to the global navigation satellite
systems (GNSS), can still operate independently in the GPS-denied environment.
Recent deep learning based methods attribute this as the task of image matching
and retrieval. By retrieving drone-view images in geo-tagged satellite image
database, approximate localization information can be obtained. However, due to
high costs and privacy concerns, it is usually difficult to obtain large
quantities of drone-view images from a continuous area. Existing drone-view
datasets are mostly composed of small-scale aerial photography with a strong
assumption that there exists a perfect one-to-one aligned reference image for
any query, leaving a significant gap from the practical localization scenario.
In this work, we construct a large-range contiguous area UAV geo-localization
dataset named GTA-UAV, featuring multiple flight altitudes, attitudes, scenes,
and targets using modern computer games. Based on this dataset, we introduce a
more practical UAV geo-localization task including partial matches of
cross-view paired data, and expand the image-level retrieval to the actual
localization in terms of distance (meters). For the construction of drone-view
and satellite-view pairs, we adopt a weight-based contrastive learning
approach, which allows for effective learning while avoiding additional
post-processing matching steps. Experiments demonstrate the effectiveness of
our data and training method for UAV geo-localization, as well as the
generalization capabilities to real-world scenarios.