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