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--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: metadata |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 6174743543.802 |
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num_examples: 1142 |
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download_size: 6408762030 |
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dataset_size: 6174743543.802 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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# GeoBench (GeoVista Bench) |
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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. |
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## Dataset Structure |
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- `id`: unique identifier (same as `uid` from the original data). |
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- `raw_image_path`: relative path (within this repo) to the source panorama under `raw_image/<uid>/`. |
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- `preview`: compressed JPEG preview (<=1M pixels) under `preview_image/<uid>/`. This is used by HF Dataset Viewer. |
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- `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. |
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- `data_type`: string describing the imagery type. If absent in metadata it defaults to `panorama`. |
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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`. |
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## Working with GeoBench |
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1. Clone/download this folder (or pull it via `huggingface_hub`). |
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2. Load the parquet file using Python: |
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```python |
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from datasets import load_dataset |
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ds = load_dataset('path/to/this/folder', split='train') |
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sample = ds[0] |
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`` |
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`sample["preview"]` loads directly as a PIL image; `sample["raw_image_path"]` points to the higher-quality file if needed. |
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3. Use the metadata to drive evaluation logic, e.g., compute city-level accuracy, filter by `data_type`, or inspect specific regions. |
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## Notes |
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- Raw panoramas retain original filenames to preserve provenance. |
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- Preview images are resized to reduce storage costs while remaining representative of the scene. |
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- Ensure you comply with the dataset’s license (`dataset_info.json`) when sharing or modifying derived works. |
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## Related Resources |
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• GeoVista model (RL-trained agentic VLM used in the paper): |
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https://huggingface.co/LibraTree/GeoVista |
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• GeoVista-Bench (previewable variant): |
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A companion dataset with resized JPEG previews intended to make image preview easier in the Hugging Face dataset viewer: |
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https://huggingface.co/datasets/LibraTree/GeoVistaBench |
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(Same underlying benchmark; different packaging / image formats.) |
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• Paper page on Hugging Face: |
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https://huggingface.co/papers/2511.15705 |
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## Citation |
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|
``` |
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@misc{wang2025geovistawebaugmentedagenticvisual, |
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title = {GeoVista: Web-Augmented Agentic Visual Reasoning for Geolocalization}, |
|
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author = {Yikun Wang and Zuyan Liu and Ziyi Wang and Pengfei Liu and Han Hu and Yongming Rao}, |
|
|
year = {2025}, |
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eprint = {2511.15705}, |
|
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archivePrefix= {arXiv}, |
|
|
primaryClass = {cs.CV}, |
|
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url = {https://arxiv.org/abs/2511.15705}, |
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} |
|
|
``` |
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|
