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---
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},
}
``` |