Add dataset card

README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - image-to-text
+  - visual-question-answering
+  - other
+tags:
+  - geolocation
+  - street-view
+  - navigation
+  - embodied-reasoning
+  - panorama
+  - benchmark
+  - geoaot
+  - cvpr2026
+language:
+  - en
+pretty_name: "WanderBench: Global Geolocation Benchmark for Actionable Reasoning"
+size_categories:
+  - 1K<n<10K
+---
+
+# WanderBench: Learning to Wander
+
+### Improving the Global Image Geolocation Ability of LMMs via Actionable Reasoning
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2603.10463"><img src="https://img.shields.io/badge/arXiv-2603.10463-b31b1b.svg" alt="arXiv"></a>
+  <a href="#"><img src="https://img.shields.io/badge/CVPR%20Findings-2026-4b44ce.svg" alt="CVPR Findings 2026"></a>
+  <a href="https://creativecommons.org/licenses/by/4.0/"><img src="https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg" alt="License: CC BY 4.0"></a>
+</p>
+
+## Dataset Description
+
+**WanderBench** is the first open-access global geolocation benchmark designed for actionable geolocation reasoning in embodied scenarios. It contains **1,049 navigable panorama graphs** comprising **32,776 panoramic nodes** distributed across six continents. Each graph encodes spatial relationships between street-view panoramas, enabling multi-step interactive exploration for geolocation tasks.
+
+This dataset accompanies the paper *"Learning to Wander: Improving the Global Image Geolocation Ability of LMMs via Actionable Reasoning"* (CVPR Findings 2026).
+
+## Dataset Statistics
+
+| Statistic | Value |
+|-----------|-------|
+| Total graphs | 1,049 |
+| Total panorama nodes | 32,776 |
+| Max graph size | 30 nodes |
+| Navigation steps per graph | up to 10 |
+| Geographic coverage | 6 continents |
+| Latitude range | ~-43° to ~54° |
+| Longitude range | ~-123° to ~154° |
+
+## Data Format
+
+Each file is a JSON graph named `{pano_id}_10_graph.json` with the following structure:
+
+```json
+{
+  "center_pano_id": "pano_id_123",
+  "max_steps": 10,
+  "nodes": [
+    {
+      "pano_id": "pano_id_123",
+      "matrix_id": 0,
+      "coordinate": {
+        "lat": 40.7128,
+        "lon": -74.0060,
+        "heading": 1.5708,
+        "roll": 0.017,
+        "pitch": -0.058
+      }
+    }
+  ],
+  "adjacency_matrix": [
+    [-1, 1.57, 0.0],
+    [4.71, -1, 1.57],
+    [3.14, 4.71, -1]
+  ]
+}
+```
+
+### Fields
+
+- **`center_pano_id`** — Google Street View panorama ID of the starting node.
+- **`max_steps`** — Maximum navigation steps allowed in the graph.
+- **`nodes`** — List of panorama nodes, each containing:
+  - `pano_id` — Street View panorama identifier.
+  - `matrix_id` — Index in the adjacency matrix.
+  - `coordinate` — GPS location (`lat`, `lon` in degrees) and camera orientation (`heading`, `roll`, `pitch` in radians).
+- **`adjacency_matrix`** — Directional angles (radians) between connected nodes; `-1` indicates no direct connection.
+
+## Usage
+
+This dataset provides the graph structure for the WanderBench benchmark. To run evaluations, clone the companion code repository:
+
+```bash
+git clone https://github.com/YushuoZheng/WanderBench.git
+```
+
+See the [code repository](https://github.com/YushuoZheng/WanderBench) for instructions on running GeoAoT exploration, baseline prediction, and batch geocoding.
+
+## Citation
+
+If you find WanderBench useful in your research, please cite:
+
+```bibtex
+@misc{zheng2026learningwanderimprovingglobal,
+      title={Learning to Wander: Improving the Global Image Geolocation Ability of LMMs via Actionable Reasoning},
+      author={Yushuo Zheng and Huiyu Duan and Zicheng Zhang and Xiaohong Liu and Xiongkuo Min},
+      year={2026},
+      eprint={2603.10463},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2603.10463},
+}
+```
+
+## License
+
+This dataset is released under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.