# 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//`. - `id`: unique identifier. - `prompt`: textual user query. - `preview`: compressed JPEG preview (<=1M pixels) under `preview_image//`. 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}, } ```