title: Game4Loc: A UAV Geo-Localization Benchmark from Game Data
summary: 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.
paper_authors._id: 66f4ff00186e8a607c071ffa
paper_authors.name: Yuxiang Ji
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-26T08:22:15.266Z
paper_authors._id: 666a83e9b2d8397c1e545785
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/666a83e9b2d8397c1e545785/7PxrVl38zWUbjAsZThHHb.jpeg
paper_authors.fullname: Yuxiang Ji
paper_authors.type: user
paper_authors.user: Yux1ang
paper_authors._id: 66f4ff00186e8a607c071ffb
paper_authors.name: Boyong He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-26T10:24:25.018Z
paper_authors._id: 665dd86f8782c7ca7869fa2d
paper_authors.avatarUrl: /avatars/787e45c26ed51b9e0c19893f97804765.svg
paper_authors.fullname: heboyong
paper_authors.type: user
paper_authors.user: heboyong
paper_authors._id: 66f4ff00186e8a607c071ffc
paper_authors.name: Zhuoyue Tan
paper_authors._id: 66f4ff00186e8a607c071ffd
paper_authors.name: Liaoni Wu
paper_title: Game4Loc: A UAV Geo-Localization Benchmark from Game Data
paper_summary: 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.
paper_ai_keywords: geo-localization UAV global navigation satellite systems (GNSS) image matching image retrieval satellite image database drone-view images approximate localization semantic aerial photography Transformer-based detector weight-based contrastive learning contrastive learning post-processing matching steps Internet-scale data generalization capabilities