Game4Loc: A UAV Geo-Localization Benchmark from Game Data
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.
66f4ff00186e8a607c071ffa
Yuxiang Ji
claimed_verified
2024-09-26T08:22:15.266Z
666a83e9b2d8397c1e545785
https://cdn-avatars.huggingface.co/v1/production/uploads/666a83e9b2d8397c1e545785/7PxrVl38zWUbjAsZThHHb.jpeg
Yuxiang Ji
user
Yux1ang
66f4ff00186e8a607c071ffb
Boyong He
admin_assigned
2024-09-26T10:24:25.018Z
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heboyong
user
heboyong
66f4ff00186e8a607c071ffc
Zhuoyue Tan
66f4ff00186e8a607c071ffd
Liaoni Wu
Game4Loc: A UAV Geo-Localization Benchmark from Game Data
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.
A new dataset and contrastive learning approach improve UAV geo-localization by handling partial matches and expanding image retrieval to distance-based localization.
geo-localization UAV GPS-denied image matching image retrieval GTA-UAV weight-based contrastive learning