---
license: cc-by-4.0
task_categories:
  - image-to-3d
  - object-detection
tags:
  - building
  - urban
  - los-angeles
  - 3d-tiles
  - window-view
  - cesium
  - street-view
  - architecture
size_categories:
  - 10K<n<100K
language:
  - en
pretty_name: LA Windowview
dataset_info:
  features:
    - name: osm_id
      dtype: string
    - name: building_type
      dtype: string
    - name: image
      dtype: image
  splits:
    - name: train
      num_examples: 37569
---

# LA Windowview

A synthetic multi-view image dataset of **1,058 mid- and high-rise buildings** in Los Angeles, CA. Each building is captured from multiple viewpoints simulating window-level perspectives across different facades and floor heights, rendered from Google Photorealistic 3D Tiles via Cesium.

## Dataset Summary

| Item | Value |
|---|---|
| Buildings | 1,058 |
| Mid-rise | 1,051 |
| High-rise | 7 |
| Total images | 37,569 |
| Image format | JPEG |
| Building source | OpenStreetMap |
| 3D rendering | Google Photorealistic 3D Tiles (Cesium) |
| Region | Los Angeles, CA, USA |

## Dataset Structure

```
LA_Windowview/
├── LA_Building_folder/        # 1,058 GeoJSON files (one per building)
│   ├── {osm_id}.geojson
│   └── ...
└── screenshots/               # 37,569 JPEG images
    ├── {osm_id}_building_{building_id}_facade_{facade_id}_floor_{floor_idx}_view_{view_id}.jpg
    └── ...
```

### GeoJSON Schema

Each `.geojson` file is a `FeatureCollection` with building footprint geometry and viewpoint metadata:

```json
{
  "type": "FeatureCollection",
  "features": [{
    "type": "Feature",
    "geometry": { "type": "Polygon", "coordinates": [...] },
    "properties": {
      "source": "osm",
      "id": "1013455134",
      "building_type": "mid-rise",
      "osm_type": "commercial",
      "name": "...",
      "centroid_lon": -118.305,
      "centroid_lat": 34.090,
      "viewpoint_count": 70,
      "processed_building_id": 17702,
      "viewpoints": [
        {
          "view_id": "building_17702_facade_0_floor_0_view_0",
          "building_id": 17702,
          "facade_id": 0,
          "floor_idx": 0,
          "floor_name": "base",
          "lon": -118.30571,
          "lat": 34.09067,
          "alt_m": 100.0,
          "heading": 0.385,
          "pitch": 0.0,
          "roll": 0.0,
          "fov_deg": 60.0
        }
      ]
    }
  }]
}
```

### Image Naming Convention

```
{osm_id}_building_{building_id}_facade_{facade_id}_floor_{floor_idx}_view_{view_id}.jpg
```

| Field | Description |
|---|---|
| `osm_id` | OpenStreetMap building ID |
| `building_id` | Internal building index |
| `facade_id` | Facade index (0-based, one per building side) |
| `floor_idx` | Floor level index of the viewpoint |
| `view_id` | View index within the facade/floor combination |

## Data Collection

Viewpoints are programmatically placed around each building at multiple facade directions and floor heights. Screenshots are rendered using CesiumJS with Google Photorealistic 3D Tiles, capturing the building appearance as seen from a neighboring window perspective.

Camera parameters per viewpoint:
- **Altitude**: 100 m above ground
- **FOV**: 60°
- **Pitch / Roll**: 0° (level horizon)
- **Heading**: oriented toward building centroid

## Usage

```python
import json, os
from PIL import Image

# Load building metadata
with open("LA_Building_folder/1013455134.geojson") as f:
    building = json.load(f)

props = building["features"][0]["properties"]
print(props["building_type"], props["viewpoint_count"])

# Load corresponding images
osm_id = props["id"]
images = [f for f in os.listdir("screenshots") if f.startswith(osm_id)]
img = Image.open(f"screenshots/{images[0]}")
img.show()
```

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{la_windowview_2026,
  author    = {Zongrong},
  title     = {LA Windowview: Multi-View Window-Level Imagery of Los Angeles Buildings},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/Zongrong/LA_Windowview}
}
```

## License

[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)