Update README.md

README.md

@@ -17,3 +17,62 @@ configs:
   - split: train
     path: data/train-*
 ---
+
+
+# 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},
+}
+```