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- ---
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- license: cc-by-4.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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+
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+ # PM25Vision
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+
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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.
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+
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+ ## Tasks
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+ - **Regression**: Predict continuous PM2.5 values.
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+ - **Classification**: Predict discrete AQI levels.
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+
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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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+
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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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+
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+ ## Dataset Structure
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+
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+ The dataset is organized into two main splits: **train** and **test**, each containing:
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+
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+ - **`images/`**: all image files used in the dataset.
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+ - **`samples_by_bin/`**: a small set of 30 example images per AQI bin (for quick visual inspection).
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+ - **`metadata.csv`**: a CSV file describing metadata (including pm2.5 labels) for each image.
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+
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+ ### Metadata Fields
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+
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+ Each row in `metadata.csv` contains:
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+
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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 value of the day that the image was captured(the label, in AQI value). |
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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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+
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+ ### Splits
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+
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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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+
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+
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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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+
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+ ## Access
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+ - Arxiv: ...
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+ - Online demo: [pm25vision.com](http://www.pm25vision.com)
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+
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+ ## Citation
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+ ```bibtex
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+ @misc{pm25vision2025,
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+ title = {PM25Vision: Street-level imagery with PM2.5 annotations},
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+ author = {Han, Yang},
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+ year = {2025},
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+ publisher = {Hugging Face Datasets},
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+ url = {https://huggingface.co/datasets/DeadCardassian/PM25Vision}
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+ }
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+ ```