| # 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 PIL image; `sample["raw_image_path"]` points to the higher-quality file if needed. | |
| 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}, | |
| } | |
| ``` | |