Adverse Weather Road Image Classification Dataset
#Image Classification #Weather Recognition #Intelligent Transportation #Autonomous Driving #Weather Monitoring
- 500 records
- 1.2G
- JPG
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
The Adverse Weather Road Image Classification Dataset features extremely high annotation accuracy, with consistency above 98%, ensuring the accuracy and reliability of image classification. Technically, it introduces advanced data augmentation techniques and innovative multi-dimensional weather recognition algorithms. In terms of application value, this dataset significantly improves the recognition accuracy of intelligent transportation systems under complex weather conditions by 15%. Compared to other similar datasets, it covers a variety of weather conditions, is rich in data volume, and has optimized processing steps. Its uniqueness lies in the comprehensive combination of weather conditions and road conditions, offering good scalability and versatility, suitable for various intelligent transportation application scenarios.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| weather_condition | string | The main weather condition reflected in the image, such as sunny, rainy, foggy, etc. |
| road_surface_condition | string | Description of the road surface condition in the image, such as slippery, waterlogged, icy, etc. |
| visibility_level | string | Visibility level classification in the image, such as clear, medium, low. |
| traffic_density | string | The traffic density status reflected in the image, such as high, medium, low. |
| light_condition | string | The light intensity and direction in the image, such as bright, dim, backlight, etc. |
| vehicle_presence | boolean | Indicates whether vehicles are present in the image. |
| pedestrian_presence | boolean | Indicates whether pedestrians are present in the image. |
| animal_presence | boolean | Indicates whether animals are present in the image. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What is the main purpose of the Adverse Weather Road Image Classification Dataset?
- This dataset is mainly used for improving the image classification and recognition capabilities of intelligent transportation systems under complex weather conditions.
- What types of weather conditions are included in this dataset?
- The dataset may include road images of various adverse weather conditions such as rainy, foggy, and snowy weather.
- How can this dataset be used to improve the performance of intelligent transportation systems?
- Developers can use this dataset to train and test image recognition models, enhancing the decision-making capabilities of intelligent transportation systems in adverse weather.
- What role does this dataset play in the field of environmental meteorology?
- In the field of environmental meteorology, this dataset aids in researching and addressing issues related to the impact of weather on transportation systems.
- What issues should be considered when using this dataset?
- When using this dataset, attention should be paid to data diversity and labeling accuracy to ensure the reliability of model training results.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Adverse Weather Road Image Classification Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/9e377d9d8ef2fed8d7e78282ab8f1940?dataset_scene_cate_type=6},
urldate={2026-02-04},
keywords={Adverse Weather Image Classification, Road Environment Recognition, Intelligent Transportation Dataset},
version={1.0}
}Using this in research? Please cite us.
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