@inproceedings{681b2f4cbb584b33adb18adbae040510,
title = "Unsupervised Image Dataset Annotation Framework for Snow Covered Road Networks",
abstract = "Road surface condition estimation plays a crucial role in road safety and maintenance, especially in adverse weather conditions like snowfall. In this paper, we introduce a framework for unsupervised annotation of a dataset describing road snow cover level. This framework relies on feature learning using autoencoders and graph clustering using the Louvain community detection algorithm. We also incorporate time and weather data to facilitate the annotation process. We evaluate our method by assessing its different steps and comparing it to another density-based clustering method. We also present a large image dataset describing four road cover states in urban scenes, including different weather and visual conditions. The dataset comprises 41346 images collected from road monitoring cameras installed in Montreal, Canada, during the 2022 winter season. This dataset intends to help integrate computer vision techniques in planning snow removal operations.",
keywords = "Autoencoders, Community Detection, Intelligent Transportation, Road Surface Condition, Unsupervised Annotation",
author = "Mohamed Karaa and Hakim Ghazzai and Lokman Sboui and Hichem Besbes and Yehia Massoud",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2022 ; Conference date: 11-11-2022 Through 13-11-2022",
year = "2022",
doi = "10.1109/APCCAS55924.2022.10090274",
language = "English (US)",
series = "APCCAS 2022 - 2022 IEEE Asia Pacific Conference on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "110--114",
booktitle = "APCCAS 2022 - 2022 IEEE Asia Pacific Conference on Circuits and Systems",
address = "United States",
}