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.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+streetview/metadata_satellite_covered.csv filter=lfs diff=lfs merge=lfs -text
+metadata/large_area_train_map.csv filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: cc-by-sa-4.0
+pretty_name: Just Zoom In Cross-View Geo-Localization Dataset
+task_categories:
+- image-retrieval
+- computer-vision
+- robotics
+task_ids:
+- visual-place-recognition
+language:
+- en
+tags:
+- cross-view-geo-localization
+- visual-localization
+- geolocation
+- satellite-imagery
+- aerial-imagery
+- street-view
+- mapillary
+- open-data-dc
+- tiledwebmaps
+- autoregressive-zooming
+size_categories:
+- 100K<n<1M
+---
+
+# Just Zoom In Cross-View Geo-Localization Dataset
+
+This repository contains the dataset used in **Just Zoom In: Cross-View Geo-Localization via Autoregressive Zooming**.
+
+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 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.
+
+## Paper
+
+**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
+
+```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}
+}
+```
+
+## Data Sources and Licensing
+
+This dataset contains data derived from two sources.
+
+| Component | Source | License |
+|---|---|---|
+| Street-view images | Mapillary | CC BY-SA 4.0 |
+| Street-view metadata derived from Mapillary imagery | Mapillary / dataset authors | CC BY-SA 4.0 |
+| Aerial orthophotography / satellite tile imagery | Open Data DC / Government of the District of Columbia | CC BY 4.0 |
+| Split files and derived benchmark metadata | Dataset authors | CC BY-SA 4.0 |
+
+Because the repository contains Mapillary-derived imagery, the dataset is distributed under **CC BY-SA 4.0**.
+
+Required source attributions:
+
+```text
+Street-view imagery derived from Mapillary, licensed under CC BY-SA 4.0.
+Aerial orthophotography derived from Open Data DC / Government of the District of Columbia, licensed under CC BY 4.0.
+```
+
+License references:
+
+- Mapillary open imagery license: https://help.mapillary.com/hc/en-us/articles/115001770409-CC-BY-SA-license-for-open-data
+- CC BY-SA 4.0: https://creativecommons.org/licenses/by-sa/4.0/
+- Open Data DC: https://opendata.dc.gov/
+- CC BY 4.0: https://creativecommons.org/licenses/by/4.0/
+
+Users are responsible for complying with the licenses of the underlying data sources.
+
+## Repository Layout
+
+```text
+.
+├── README.md
+├── metadata/
+│   ├── large_area_train_map.csv
+│   └── large_area_val_map.csv
+├── streetview/
+│   ├── metadata_satellite_covered.csv
+│   └── metadata_satellite_covered.parquet
+├── satellite/
+│   └── layout.yaml
+└── archives/
+    ├── streetview_images_000.tar
+    ├── streetview_images_001.tar
+    ├── streetview_images_002.tar
+    ├── streetview_images_003.tar
+    ├── satellite_level_0_000.tar
+    ├── satellite_level_0_001.tar
+    ├── ...
+    ├── satellite_level_m1_000.tar
+    ├── satellite_level_m2_000.tar
+    └── ...
+```
+
+The archive names use `m1`, `m2`, etc. for negative satellite levels. For example, `satellite_level_m1_000.tar` contains files under `satellite/-1/`.
+
+After extraction, the data has this structure:
+
+```text
+extracted/
+├── streetview/
+│   └── images/
+│       ├── <image_id>_undistorted.jpg
+│       └── ...
+└── satellite/
+    ├── layout.yaml
+    ├── 0/
+    ├── -1/
+    ├── -2/
+    ├── ...
+    └── -9/
+```
+
+## Metadata
+
+The split CSV files contain the fields used by the paper's training/evaluation loader.
+
+| Field | Description |
+|---|---|
+| `image_id` | Ground-level image identifier |
+| `sequence` | Ground-truth zoom-action sequence |
+| `latitude` | Ground-truth latitude |
+| `longitude` | Ground-truth longitude |
+
+Ground images are named:
+
+```text
+<image_id>_undistorted.jpg
+```
+
+## Download and Extract
+
+
+```python
+from pathlib import Path
+import shutil
+import tarfile
+
+from huggingface_hub import snapshot_download
+
+repo_dir = Path(snapshot_download(
+    repo_id="pcvlab/justzoomin",
+    repo_type="dataset",
+))
+
+extract_dir = Path("./justzoomin_data")
+extract_dir.mkdir(parents=True, exist_ok=True)
+
+for tar_path in sorted((repo_dir / "archives").glob("*.tar")):
+    print(f"Extracting {tar_path.name}")
+    with tarfile.open(tar_path, "r") as tar:
+        tar.extractall(extract_dir)
+
+# Place the tiledwebmaps layout file beside the extracted satellite folders.
+(extract_dir / "satellite").mkdir(exist_ok=True)
+shutil.copy2(
+    repo_dir / "satellite" / "layout.yaml",
+    extract_dir / "satellite" / "layout.yaml",
+)
+```
+
+## Loader Paths
+
+After extraction, use the following paths in the paper's dataset loader:
+
+```python
+metadata_csv = repo_dir / "metadata" / "large_area_train_map.csv"
+ground_root = extract_dir / "streetview" / "images"
+tile_layout = extract_dir / "satellite" / "layout.yaml"
+```
+
+For validation:
+
+```python
+metadata_csv = repo_dir / "metadata" / "large_area_val_map.csv"
+```
+
+The satellite tile folders are loaded through `satellite/layout.yaml` using `tiledwebmaps`.
+
+## 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.
+
+The data is not stored as loose image files on the Hub. The TAR shards should be extracted before using the original path-based loader.
+
+## Citation
+
+If you use this dataset, cite the paper:
+
+```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}
+}
+```
+
+Please also attribute the underlying data sources:
+
+```text
+Street-view imagery: Mapillary, CC BY-SA 4.0.
+Aerial orthophotography: Open Data DC / Government of the District of Columbia, CC BY 4.0.
+```

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+path: '{zoom}/{x}/{y}.jpg'
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