README.md

metadata

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

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:

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
• Online demo: pm25vision.com
• Kaggle (Download the entire data folder in a zip file, suitable for expansion needs): PM25Vision

Citation

@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}, 
}