Datasets:

LibraTree
/

GeoVistaBench

GeoVistaBench is a collection of real-world photos with rich metadata for evaluating geolocation models. Each sample corresponds to one picture identified by its uid and includes both the original high-resolution imagery and a lightweight preview for rapid inspection.

Dataset Structure

id: unique identifier.
raw_image_path: relative path (within this repo) to the source picture under raw_image/<uid>/.
preview: compressed JPEG preview (<=1M pixels) under preview_image/<uid>/. This is used by HF Dataset Viewer.
metadata: downstream users can parse it to obtain lat/lng, city names, multi-level location tags, and related information.
data_type: string describing the imagery type.

All samples are stored in a Hugging Face-compatible parquet file.

Working with GeoBench

Clone/download this folder (or pull it via huggingface_hub).

Load the parquet file using Python:

from datasets import load_dataset

ds = load_dataset('path/to/this/folder', split='test')
sample = ds[0]
``
`sample["raw_image_path"]` points to the higher-quality file for inference.

Related Resources

GeoVista Technical Report https://huggingface.co/papers/2511.15705
GeoVista-Bench (previewable variant): A companion dataset with resized JPEG previews intended to make image preview easier in the Hugging Face dataset viewer: https://huggingface.co/datasets/LibraTree/GeoVistaBench (Same underlying benchmark; different packaging / image formats.)

Citation

@misc{wang2025geovistawebaugmentedagenticvisual,
      title        = {GeoVista: Web-Augmented Agentic Visual Reasoning for Geolocalization},
      author       = {Yikun Wang and Zuyan Liu and Ziyi Wang and Pengfei Liu and Han Hu and Yongming Rao},
      year         = {2025},
      eprint       = {2511.15705},
      archivePrefix= {arXiv},
      primaryClass = {cs.CV},
      url          = {https://arxiv.org/abs/2511.15705},
}