| # GeoVistaBench |
|
|
| **GeoVistaBench is the first benchmark to evaluate agentic models’ general geolocalization ability.** |
|
|
| 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 |
|
|
| - `raw_image_path`: relative path (within this repo) to the source picture under `raw_image/<uid>/`. |
| - `id`: unique identifier. |
| - `prompt`: textual user query. |
| - `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 |
|
|
| 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='test') |
| sample = ds[0] |
| ``` |
|
|
| **sample["raw_image_path"]` points to the higher-quality image 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 Han Hu and Pengfei Liu and Yongming Rao}, |
| year={2025}, |
| eprint={2511.15705}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2511.15705}, |
| } |
| ``` |
|
|
