Mobiusi/Highway-Visibility-Estimation-under-Adverse-Weather-Video-Dataset

Highway Visibility Estimation under Adverse Weather Video Dataset

Currently, accurate visibility estimation is crucial for traffic safety and weather forecasting in the environmental meteorological industry. However, the rapid changes in visibility under adverse weather present significant challenges to existing estimation models, with many traditional methods struggling to achieve ideal results due to a lack of high-quality adverse weather data. This dataset aims to provide a rich and diverse video library focusing on changes in highway visibility under various types of adverse weather conditions, supporting the development of more advanced machine learning algorithms. Data is collected using high-resolution camera equipment, covering various weather conditions such as heavy fog, torrential rain, and blizzards, shot in real highway environments. Regarding quality control, the dataset has undergone multiple rounds of annotation and consistency checks, and has been reviewed by an expert team from the field of meteorology and traffic safety, ensuring annotation accuracy of no less than 95%. The annotation team consists of 20 professionals with a background in meteorology. Data preprocessing includes noise filtering, frame deduplication, etc., organized in 'MP4' format storage. Each video segment in the dataset corresponds to weather conditions and visibility labels, facilitating retrieval and analysis. The quality of the dataset lies in its high annotation accuracy and relevance to practical applications, providing over 95% consistency and integrity assurance. Through innovative data augmentation methods like synthetic weather scenes, this dataset excels in addressing hard-to-capture extreme weather conditions, significantly improving the accuracy of visibility estimation models under adverse conditions, with performance improvements near 30% over traditional datasets. As a valuable resource for the environmental and transportation fields, it features high coverage and scarcity of weather scenes, with wide applicability, filling gaps in existing datasets, showing unique advantages when used for training models with strong generalization abilities. Its flexible structure design also supports easy integration of incremental data for future weather conditions and scenes.

Technical Specifications

Field	Type	Description
file_name	string	File name
duration	string	Duration
quality	string	Resolution
weather_condition	string	The specific adverse weather condition present in the video, such as fog, rain, snow, etc.
visibility_level	string	The visibility level observed in the video, categorized as good, moderate, or low.
road_type	string	The type of road captured in the video, such as highway, city road, country road, etc.
traffic_density	string	The density of vehicle flow in the video, categorized as high density, medium density, or low density.
day_night	string	The lighting condition during video capture, determined as daytime or nighttime.
light_conditions	string	The lighting condition in the video, such as sunny, cloudy, or dusk.
camera_angle	string	The angle of the camera in the video, such as front, side, or aerial view.
precipitation_intensity	string	The intensity of precipitation recorded in the video, categorized as none, light, moderate, or heavy.
road_markings_visibility	string	The visibility of road markings in the video, assessed as clear, blurry, or not visible.

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

Source & Contact

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