# GeoBench (GeoVista Bench)

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

## Dataset Structure

- `id`: unique identifier (same as `uid` from the original data).
- `raw_image_path`: relative path (within this repo) to the source panorama under `raw_image/<uid>/`.
- `preview`: compressed JPEG preview (<=1M pixels) under `preview_image/<uid>/`. This is used by HF Dataset Viewer.
- `metadata`: JSON object storing capture timestamp, location, pano_id, city, and other attributes. Downstream users can parse it to obtain lat/lng, city names, multi-level location tags, etc.
- `data_type`: string describing the imagery type. If absent in metadata it defaults to `panorama`.

All samples are stored in a Hugging Face-compatible parquet file at `data/<split>/data-00000-of-00001.parquet`, with additional metadata in `dataset_info.json`.

## Working with GeoBench

1. Clone/download this folder (or pull it via `huggingface_hub`).
2. Load the parquet file using Python:
   ```python
   from datasets import load_dataset

   ds = load_dataset('path/to/this/folder', split='train')
   sample = ds[0]
   ``
   `sample["preview"]` loads directly as a compressed PIL image; `sample["raw_image_path"]` points to the higher-quality file for inference.
3. Use the metadata to drive evaluation logic, e.g., compute city-level accuracy, filter by `data_type`, or inspect specific regions.

## Notes

- Raw panoramas retain original filenames to preserve provenance.
- Preview images are resized to reduce storage costs while remaining representative of the scene.
- Ensure you comply with the dataset’s license (`dataset_info.json`) when sharing or modifying derived works.

## Related Resources

- GeoVista model (RL-trained agentic VLM used in the paper):
https://huggingface.co/LibraTree/GeoVista

- 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.)

- Paper page on Hugging Face:
https://huggingface.co/papers/2511.15705


## 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},
}
```