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--- |
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pretty_name: PM25Vision |
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tags: |
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- computer-vision |
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- pm2.5 |
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- regression |
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- classification |
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- air-quality |
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- AQI |
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task_categories: |
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- image-classification |
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- other |
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license: cc-by-4.0 |
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language: |
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- en |
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size_categories: |
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- 10K<n<100K |
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--- |
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# PM25Vision |
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## Dataset Summary |
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PM25Vision (PM25V) is a large-scale dataset for estimating air quality (PM2.5) from street-level imagery. It pairs **Mapillary** photos with **World Air Quality Index (WAQI)** PM2.5 records, covering 2014–2025, 3,261 monitoring stations, and 11,114 cleaned and balanced images with PM2.5 AQI labels. |
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## Tasks |
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- **Regression**: Predict continuous PM2.5 **AQI** values. |
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- **Classification**: Predict discrete AQI levels. |
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## Baseline Results |
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### Regression |
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| Model | R² | MAE | RMSE | Acc | F1 | |
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|-----------------|------|------|------|------|------| |
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| EfficientNet-B0 | 0.55 | 36.6 | 54.6 | 0.46 | 0.45 | |
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| ResNet50 | 0.50 | 38.6 | 57.5 | 0.44 | 0.35 | |
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| ViT-B/16 | 0.23 | 50.3 | 71.7 | 0.35 | 0.30 | |
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### Classification |
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| Model | Acc | F1 | Precision | Recall | |
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|-----------------|------|------|-----------|--------| |
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| ResNet50 | 0.44 | 0.38 | 0.48 | 0.37 | |
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| ViT-B/16 | 0.40 | 0.37 | 0.41 | 0.36 | |
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| EfficientNet-B0 | 0.40 | 0.34 | 0.42 | 0.33 | |
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## Usage |
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### Quick Start |
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```python |
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import torch |
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import torch.nn as nn |
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import torch.optim as optim |
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from datasets import load_dataset |
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from torch.utils.data import DataLoader |
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import torchvision.transforms as T |
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from PIL import Image |
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from io import BytesIO |
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# ===== Load dataset ===== |
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ds = load_dataset("DeadCardassian/PM25Vision") |
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transform = T.Compose([ |
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T.Resize((224, 224)), |
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T.ToTensor(), |
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]) |
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def collate_fn(batch): |
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imgs = [transform(Image.open(BytesIO(x["image"])).convert("RGB")) for x in batch] |
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labels = [x["pm25"] for x in batch] # pm25 AQI value |
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return torch.stack(imgs), torch.tensor(labels, dtype=torch.float32) |
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train_loader = DataLoader(ds["train"], batch_size=32, shuffle=True, collate_fn=collate_fn) |
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# ===== Simple CNN ===== |
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class SimpleCNN(nn.Module): |
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def __init__(self): |
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super().__init__() |
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self.net = nn.Sequential( |
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nn.Conv2d(3, 16, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), |
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nn.Conv2d(16, 32, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), |
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nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(), nn.AdaptiveAvgPool2d(1), |
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) |
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self.fc = nn.Linear(64, 1) # regression |
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def forward(self, x): |
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x = self.net(x) |
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x = x.view(x.size(0), -1) |
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return self.fc(x).squeeze(1) |
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# ===== Training loop ===== |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model = SimpleCNN().to(device) |
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optimizer = optim.Adam(model.parameters(), lr=1e-3) |
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criterion = nn.MSELoss() |
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for epoch in range(5): # 5 epoch for demo |
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for imgs, labels in train_loader: |
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imgs, labels = imgs.to(device), labels.to(device) |
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optimizer.zero_grad() |
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outputs = model(imgs) |
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loss = criterion(outputs, labels) |
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loss.backward() |
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optimizer.step() |
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print(f"Epoch {epoch+1}: train loss = {loss.item():.4f}") |
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``` |
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Notes: |
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To switch from AQI values (regression) to AQI levels (classification), simply add a mapping like: |
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```python |
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def map_pm25_to_class(pm25): |
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if pm25 <= 50.4: return 0 |
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elif pm25 <= 100.4: return 1 |
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elif pm25 <= 150.4: return 2 |
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elif pm25 <= 200.4: return 3 |
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elif pm25 <= 300.4: return 4 |
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else: return 5 |
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``` |
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### Label Fields |
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| Field | Type | Description | |
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|----------------|---------|----------------------------------------------------------------------| |
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| `**image_id**` | int64 | Unique image identifier (from Mapillary). | |
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| `station_id` | int64 | WAQI monitoring station ID. | |
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| `captured_at` | object | Date when the image was captured (YYYY-MM-DD). | |
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| `camera_angle` | float64 | Camera orientation (if available). | |
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| `longitude` | float64 | Longitude of the station. | |
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| `latitude` | float64 | Latitude of the station. | |
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| `quality_score`| float64 | Image quality score from Mapillary (if available). | |
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| `downloaded_at`| object | Timestamp when the sample was downloaded. | |
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| `**pm25**` | float64 | Average PM2.5 AQI value of the day that the image was captured. | |
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| `filename` | object | Image filename, located in the `images/` directory. | |
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| `quality` | object | ResNet18 classified label for image quality (e.g., `good` or `bad`). | |
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| `pm25_bin` | object | Discrete AQI level label (e.g., `0–50`, `51–100`, etc.). | |
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**Only `image_id` and `pm25` will be used most of the time.** |
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### Splits |
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- **Train**: 80% of samples, balanced across AQI bins. |
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- **Test**: 20% of samples, balanced across AQI bins. |
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## Limitations |
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- WAQI temporal resolution is **daily**, may miss intra-day variation. |
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- Spatial accuracy limited to 5 km around stations. |
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- Rare extreme AQI classes remain underrepresented. |
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## Access |
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- Arxiv: [PM25Vision](https://arxiv.org/abs/2509.16519) |
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- Online demo: [pm25vision.com](http://www.pm25vision.com) |
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- Kaggle (Download the entire data folder in a zip file, suitable for expansion needs): [PM25Vision](https://www.kaggle.com/datasets/DeadCardassian/pm25vision) |
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## Citation |
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```bibtex |
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@misc{han2025pm25visionlargescalebenchmarkdataset, |
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title={PM25Vision: A Large-Scale Benchmark Dataset for Visual Estimation of Air Quality}, |
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author={Yang Han}, |
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year={2025}, |
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eprint={2509.16519}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2509.16519}, |
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} |
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``` |
