Update README.md

README.md

@@ -2,17 +2,17 @@
 
 **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 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 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
 
-- `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>/`.
+- `id`: unique identifier.
+- `raw_image_path`: relative path (within this repo) to the source picture 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`.
+- `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 at `data/<split>/data-00000-of-00001.parquet`, with additional metadata in `dataset_info.json`.
+All samples are stored in a Hugging Face-compatible parquet file.
 
 ## Working with GeoBench
 
@@ -24,18 +24,11 @@ All samples are stored in a Hugging Face-compatible parquet file at `data/<split
    ds = load_dataset('path/to/this/folder', split='test')
    sample = ds[0]
    ``
-   `sample["raw_image_path"]` points to the higher-quality file for inference. `sample["preview"]` loads directly as a compressed PIL image.
-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.
+   `sample["raw_image_path"]` points to the higher-quality file for inference.
 
 ## Related Resources
 
-- GeoVista model (RL-trained agentic VLM used in the paper):
+- GeoVista Technical Report
 https://huggingface.co/papers/2511.15705
 
 - GeoVista-Bench (previewable variant):
@@ -43,10 +36,6 @@ A companion dataset with resized JPEG previews intended to make image preview ea
 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,