%0 Dataset
%T A dataset of blowing snow event visibility classification for the Makitasi Wind Zone, China, 2024–2025
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/e483058f-0cda-40cf-9d4e-b2de153b0586
%W NCDC
%R 10.12072/ncdc.db7472.2026
%A MA Lei
%A Hu Zhixuan
%A Zhao Jing
%A Li Pengbo
%A Zhao Qin
%A Pan Xingyu
%A Li Taizhi
%K mayitas wind zone;2024-2025;wind and snow;visibility classification;Expressway
%X Low visibility induced by blowing snow disasters is a key factor affecting traffic safety and operational efficiency on high-grade highways. Fine-grained visibility grading data are of great significance for model training, control strategy formulation, and the deployment of roadside edge computing devices. Based on continuous videos from four roadside fixed monitoring cameras and visibility instrument observations along the G3015 Keketa Expressway (K264+700–K272+700) in the Mayitas Wind Zone, Xinjiang, from November 2024 to March 2025, this dataset was constructed by first temporally filtering blowing snow events, then extracting frames at a frequency of 1 frame per minute, and finally applying manual grading annotation. The dataset consists of one compressed archive containing 7 folders corresponding to 7 visibility levels (0–100 m, 100–200 m, 200–300 m, 300–400 m, 400–500 m, 500–1000 m, >500m and NULL), comprising 14,678 roadside camera images with a spatial resolution of 640×480 pixels. It also includes one visibility instrument data file in tabular format, containing time, wind speed, wind direction, minimum visibility, and average visibility. This dataset employed dual independent annotation followed by expert review. Missing and anomalous data caused by external factors were excluded without interpolation, ensuring the accuracy, authenticity, and reliability of the annotated observations. This dataset can be used to characterize the spatiotemporal distribution and variation of highway visibility under blowing snow conditions, serving traffic meteorological warning and dynamic expressway control in blowing snow-prone areas.