Update dataset README

README.md

@@ -2,16 +2,18 @@
 license: cc-by-sa-4.0
 pretty_name: Just Zoom In Cross-View Geo-Localization Dataset
 task_categories:
-- image-retrieval
-- computer-vision
+- image-to-image
+- image-feature-extraction
 - robotics
-task_ids:
-- visual-place-recognition
+- other
 language:
 - en
 tags:
 - cross-view-geo-localization
 - visual-localization
+- visual-place-recognition
+- image-retrieval
+- computer-vision
 - geolocation
 - satellite-imagery
 - aerial-imagery
@@ -25,27 +27,24 @@ size_categories:
 ---
 
 # Just Zoom In Cross-View Geo-Localization Dataset
+![Just Zoom In Dataset Preview](assets/justzoomin_trial_zoom.gif)
 
-This repository contains the dataset used in **Just Zoom In: Cross-View Geo-Localization via Autoregressive Zooming**.
+This dataset accompanies **Just Zoom In: Cross-View Geo-Localization via Autoregressive Zooming**. It supports cross-view geo-localization, where a ground-level street-view image is localized using geo-referenced overhead imagery. The dataset pairs crowd-sourced, limited-field-of-view street-view images with a multi-scale satellite/aerial tile hierarchy over Washington, D.C., making it suitable for realistic evaluation of both retrieval-based and coarse-to-fine localization methods.
 
-The dataset supports cross-view geo-localization: given a ground-level street-view image, the task is to localize it using geo-referenced overhead imagery. The dataset pairs crowd-sourced street-view images with a multi-scale satellite/aerial tile hierarchy over the Washington, D.C. area.
+The current release contains approximately 300k ground-level images over a 10 km × 10 km Washington, D.C. area, together with the corresponding multi-scale overhead tile hierarchy.
 
 The data is stored as TAR shards to keep the Hugging Face repository manageable. After extraction, the directory layout matches the path-based PyTorch/tiledwebmaps loader used in the paper.
+## Links
 
-## Paper
+- 📄 Paper: [Just Zoom In: Cross-View Geo-Localization via Autoregressive Zooming](https://arxiv.org/abs/2603.25686)
+- 💻 GitHub: [Official Repository](https://github.com/OSUPCVLab/justzoomin)
+- 🌐 [Project Website](https://osupcvlab.github.io/just-zoom-in/)
 
-**Just Zoom In: Cross-View Geo-Localization via Autoregressive Zooming**  
-Yunus Talha Erzurumlu, Jiyong Kwag, and Alper Yilmaz  
-Photogrammetric Computer Vision Lab, The Ohio State University
+## Motivation
 
-```bibtex
-@article{erzurumlu2026justzoomin,
-  title={Just Zoom In: Cross-View Geo-Localization via Autoregressive Zooming},
-  author={Erzurumlu, Yunus Talha and Kwag, Jiyong and Yilmaz, Alper},
-  journal={arXiv preprint arXiv:2603.25686},
-  year={2026}
-}
-```
+Most cross-view geo-localization benchmarks are built around single-shot retrieval from fixed satellite crops. This setup is useful, but it can underrepresent key challenges of real-world localization: street-view images may have unknown orientation, limited field of view, variable image quality, and visual cues that fall outside a single overhead crop. In addition, flat retrieval databases do not explicitly expose the geographic hierarchy of maps.
+
+This benchmark is designed to address these limitations by combining realistic crowd-sourced street-view imagery with a multi-scale overhead tile structure. It enables evaluation of methods that reason from coarse geographic context to fine local detail, including autoregressive zooming models, hierarchical search methods, and standard retrieval baselines.
 
 ## Data Sources and Licensing
 
@@ -186,7 +185,10 @@ metadata_csv = repo_dir / "metadata" / "large_area_val_map.csv"
 ```
 
 The satellite tile folders are loaded through `satellite/layout.yaml` using `tiledwebmaps`.
+## Samples from the Dataset
+![](assets/hf_readme_20_samples.jpg)
 
+Each sample consists of a ground-level street-view image, its latitude/longitude, and a ground-truth zoom-action sequence over the satellite tile hierarchy. The standard task is to predict the correct overhead cell, or equivalently the zoom-action sequence, for a given street-view query. However, users are not limited to use discrete sequences, since dataset supports many different zoom levels.
 ## Dataset Notes
 
 The benchmark uses limited-field-of-view, non-panoramic, crowd-sourced street-view images and a multi-scale overhead tile hierarchy. It is intended for within-area cross-view geo-localization over the Washington, D.C. region.