Drone-view target localization

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

9 shown of 9 papers with code (10 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization
  27 Feb 2020 3 repositories listed Syntology ran 1 of 15 samples · 14 unverified
  To our knowledge, University-1652 is the first drone-based geo-localization dataset and enables two new tasks, i.
• CV-Cities: Advancing Cross-View Geo-Localization in Global Cities
  19 Nov 2024 1 repository listed Syntology ran 0 of 9 samples · 9 unverified
  Cross-view geo-localization (CVGL), which involves matching and retrieving satellite images to determine the geographic location of a ground image, is crucial in GNSS-constrained scenarios.
• Game4Loc: A UAV Geo-Localization Benchmark from Game Data
  25 Sep 2024 1 repository listed Syntology ran 9 of 10 samples · 1 unverified
  Recent deep learning based methods attribute this as the task of image matching and retrieval.
• A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
  23 Sep 2023 1 repository listed
  The geo-localization of UAVs and satellites can be achieved by querying pre-obtained satellite images with GPS-tagged and drone images from different perspectives.
• Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation
  21 Mar 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  In this article, we present a simplified but effective architecture based on contrastive learning with symmetric InfoNCE loss that outperforms current state-of-the-art results.
• Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization
  15 May 2022 1 repository listed
  Inspired by the human visual system for mining local patterns, we propose a new framework called RK-Net to jointly learn the discriminative Representation and detect salient Keypoints with a single Network.
• A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization
  23 Jan 2022 1 repository listed
  However it still has some limitations, e.
• Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
  14 Dec 2020 1 repository listed
  We argue that the first phase equals building the k-nearest neighbor graph, while the second phase can be viewed as spreading the message within the graph.
• Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization
  26 Aug 2020 1 repository listed
  Existing methods usually concentrate on mining the fine-grained feature of the geographic target in the image center, but underestimate the contextual information in neighbor areas.

Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.