University 1652: Drone based Geo localization Benchmark 🚁 University 1652 is a multi view dataset for drone based geo localization, annotating 1652 buildings across 72 universities (ACM Multimedia 2020, paper). Cited in 50+ papers , it supports Drone → Satellite localization and Satellite → Drone navigation . 📊 Dataset Structure Splits : Train: 50,218 images (drone, satellite, street, google; 33 universities) Test: query drone: 37,855 images gallery drone: 51,355 images query street: 2,579 images gallery street: 2,921 images query satellite: 701 images gallery satellite: 951 images 4K drone: 12 images Features : image : Drone/satellite/street/4K drone images building id : Building identifier view type : drone/satellite/street/drone 4k split type : train/query/gallery Size : ~9.2GB (unzipped) 🚀 Usage (Download Only) Since University 1652 is a standard multi view vision benchmark with a strict directory structure, this repository is intended for raw file downloading only . You do not need to load it via datasets.load dataset() . Instead, download the raw folders and plug them directly into your standard PyTorch DataLoader (e.g., torchvision.datasets.ImageFolder ). Method 1: Using…
Runs entirely in your browser via DuckDB-Wasm — this dataset's real data file is loaded once, then queried locally. Nothing is sent to a server.
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