Unsupervised Image Dataset Annotation Framework for Snow Covered Road Networks

Mohamed Karaa; Hakim Ghazzai; Lokman Sboui; Hichem Besbes; Yehia Massoud

doi:10.1109/APCCAS55924.2022.10090274

Unsupervised Image Dataset Annotation Framework for Snow Covered Road Networks

Mohamed Karaa, Hakim Ghazzai, Lokman Sboui, Hichem Besbes, Yehia Massoud

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English (US)
Title of host publication	APCCAS 2022 - 2022 IEEE Asia Pacific Conference on Circuits and Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	110-114
Number of pages	5
ISBN (Electronic)	9781665450737
DOIs	https://doi.org/10.1109/APCCAS55924.2022.10090274
State	Published - 2022
Event	2022 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2022 - Virtual, Online, China Duration: Nov 11 2022 → Nov 13 2022

Publication series

Name	APCCAS 2022 - 2022 IEEE Asia Pacific Conference on Circuits and Systems

Conference

Conference	2022 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2022
Country/Territory	China
City	Virtual, Online
Period	11/11/22 → 11/13/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Autoencoders
Community Detection
Intelligent Transportation
Road Surface Condition
Unsupervised Annotation

ASJC Scopus subject areas

Hardware and Architecture
Electrical and Electronic Engineering
Artificial Intelligence
Computer Science Applications

Access to Document

10.1109/APCCAS55924.2022.10090274

Cite this

Karaa, M., Ghazzai, H., Sboui, L., Besbes, H., & Massoud, Y. (2022). Unsupervised Image Dataset Annotation Framework for Snow Covered Road Networks. In APCCAS 2022 - 2022 IEEE Asia Pacific Conference on Circuits and Systems (pp. 110-114). (APCCAS 2022 - 2022 IEEE Asia Pacific Conference on Circuits and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APCCAS55924.2022.10090274

@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.",

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Karaa, M, Ghazzai, H, Sboui, L, Besbes, H & Massoud, Y 2022, Unsupervised Image Dataset Annotation Framework for Snow Covered Road Networks. in APCCAS 2022 - 2022 IEEE Asia Pacific Conference on Circuits and Systems. APCCAS 2022 - 2022 IEEE Asia Pacific Conference on Circuits and Systems, Institute of Electrical and Electronics Engineers Inc., pp. 110-114, 2022 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2022, Virtual, Online, China, 11/11/22. https://doi.org/10.1109/APCCAS55924.2022.10090274

Unsupervised Image Dataset Annotation Framework for Snow Covered Road Networks. / Karaa, Mohamed; Ghazzai, Hakim; Sboui, Lokman et al.
APCCAS 2022 - 2022 IEEE Asia Pacific Conference on Circuits and Systems. Institute of Electrical and Electronics Engineers Inc., 2022. p. 110-114 (APCCAS 2022 - 2022 IEEE Asia Pacific Conference on Circuits and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Unsupervised Image Dataset Annotation Framework for Snow Covered Road Networks

AU - Karaa, Mohamed

AU - Ghazzai, Hakim

AU - Sboui, Lokman

AU - Besbes, Hichem

AU - Massoud, Yehia

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Autoencoders

KW - Community Detection

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Karaa M, Ghazzai H, Sboui L, Besbes H, Massoud Y. Unsupervised Image Dataset Annotation Framework for Snow Covered Road Networks. In APCCAS 2022 - 2022 IEEE Asia Pacific Conference on Circuits and Systems. Institute of Electrical and Electronics Engineers Inc. 2022. p. 110-114. (APCCAS 2022 - 2022 IEEE Asia Pacific Conference on Circuits and Systems). doi: 10.1109/APCCAS55924.2022.10090274

Unsupervised Image Dataset Annotation Framework for Snow Covered Road Networks

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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