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Effect of dataset distribution on automatic road extraction in very high-resolution orthophoto using DeepLab V3+

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Title: Effect of dataset distribution on automatic road extraction in very high-resolution orthophoto using DeepLab V3+
Authors: Sussi, Sussi, Emir, Husni, Arthur, Siburian, Rahadian, Yusuf, Agung Budi, Harto, Deni, Suwardhi
Publisher Information: Zenodo
Publication Year: 2024
Collection: Zenodo
Subject Terms: Deep learning, DeepLab V3+, Dice loss, Mean intersection over union, Road extraction
Description: Road extraction is one of the stages in the map-making process, which has been done manually, takes a long time, and costs a lot. Deep Learning is used to speed up the road extraction process by performing binary semantic segmentation on the image. We propose DeepLab V3+ to produce road extraction from very high-resolution orthophoto for Indonesia study area, which poses many challenges, such as road obstruction by trees, clouds, building shadows, dense traffic, and similarities to rivers and rice fields. We compared the distribution of datasets to obtain the optimal performance of the DeepLab V3+ model in relation to the dataset. The results showed that dataset ratio of 75:10:15 resulted in mean Intersection Over Union (mIoU) of 0.92 and Dice Loss of 0.042. Visually, the results of road extraction are more accurate when compared to the results obtained from different distributions of the dataset.
Document Type: article in journal/newspaper
Language: unknown
ISSN: 2252-8938
Relation: https://zenodo.org/records/14038266; oai:zenodo.org:14038266
DOI: 10.11591/ijai.v13.i2.pp1650-1657
Availability: https://doi.org/10.11591/ijai.v13.i2.pp1650-1657
https://zenodo.org/records/14038266
Rights: Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode
Accession Number: edsbas.9F3A2FA5
Database: BASE
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  Data: Effect of dataset distribution on automatic road extraction in very high-resolution orthophoto using DeepLab V3+
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  Data: <searchLink fieldCode="AR" term="%22Sussi%2C+Sussi%22">Sussi, Sussi</searchLink><br /><searchLink fieldCode="AR" term="%22Emir%2C+Husni%22">Emir, Husni</searchLink><br /><searchLink fieldCode="AR" term="%22Arthur%2C+Siburian%22">Arthur, Siburian</searchLink><br /><searchLink fieldCode="AR" term="%22Rahadian%2C+Yusuf%22">Rahadian, Yusuf</searchLink><br /><searchLink fieldCode="AR" term="%22Agung+Budi%2C+Harto%22">Agung Budi, Harto</searchLink><br /><searchLink fieldCode="AR" term="%22Deni%2C+Suwardhi%22">Deni, Suwardhi</searchLink>
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  Data: 2024
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  Data: Road extraction is one of the stages in the map-making process, which has been done manually, takes a long time, and costs a lot. Deep Learning is used to speed up the road extraction process by performing binary semantic segmentation on the image. We propose DeepLab V3+ to produce road extraction from very high-resolution orthophoto for Indonesia study area, which poses many challenges, such as road obstruction by trees, clouds, building shadows, dense traffic, and similarities to rivers and rice fields. We compared the distribution of datasets to obtain the optimal performance of the DeepLab V3+ model in relation to the dataset. The results showed that dataset ratio of 75:10:15 resulted in mean Intersection Over Union (mIoU) of 0.92 and Dice Loss of 0.042. Visually, the results of road extraction are more accurate when compared to the results obtained from different distributions of the dataset.
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      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: DeepLab V3+
        Type: general
      – SubjectFull: Dice loss
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      – SubjectFull: Mean intersection over union
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      – SubjectFull: Road extraction
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      – TitleFull: Effect of dataset distribution on automatic road extraction in very high-resolution orthophoto using DeepLab V3+
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              Y: 2024
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