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

TL;DR
This paper introduces GTA-UAV, a large-scale UAV geo-localization dataset created from computer game environments, and proposes a practical cross-view image retrieval method that improves UAV localization accuracy in diverse scenarios.
Contribution
The work provides a new large-scale, diverse UAV dataset from game data and develops a contrastive learning approach for cross-view geo-localization that addresses practical challenges.
Findings
The dataset covers multiple altitudes, attitudes, and scenes.
The proposed method effectively learns cross-view matching without extra post-processing.
Experiments show improved localization accuracy and generalization to real-world scenarios.
Abstract
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…
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Code & Models
Videos
Taxonomy
TopicsRobotics and Sensor-Based Localization · Artificial Intelligence in Games · Robotic Path Planning Algorithms
MethodsGreedy Policy Search · Contrastive Learning
