Update README.md

README.md

@@ -21,45 +21,17 @@ configs:
 
 # 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.
+GeoVistaBench is a collection of real-world panoramas with rich metadata for evaluating geolocation models. Each sample is a single image uniquely identified by a uid and includes both the original high-resolution panorama and a lightweight preview for fast 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.
+For the latest previewable version, please visit: https://huggingface.co/datasets/LibraTree/GeoVistaBench
 
 ## 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