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http://dx.doi.org/10.7848/ksgpc.2022.40.3.239

Road Extraction from Images Using Semantic Segmentation Algorithm  

Oh, Haeng Yeol (Dept. of Civil Engineering, Chosun University)
Jeon, Seung Bae (Dept. of Civil Engineering, Chosun University)
Kim, Geon (Dept. of Civil Engineering, Chosun University)
Jeong, Myeong-Hun (Dept. of Civil Engineering, Chosun University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.40, no.3, 2022 , pp. 239-247 More about this Journal
Abstract
Cities are becoming more complex due to rapid industrialization and population growth in modern times. In particular, urban areas are rapidly changing due to housing site development, reconstruction, and demolition. Thus accurate road information is necessary for various purposes, such as High Definition Map for autonomous car driving. In the case of the Republic of Korea, accurate spatial information can be generated by making a map through the existing map production process. However, targeting a large area is limited due to time and money. Road, one of the map elements, is a hub and essential means of transportation that provides many different resources for human civilization. Therefore, it is essential to update road information accurately and quickly. This study uses Semantic Segmentation algorithms Such as LinkNet, D-LinkNet, and NL-LinkNet to extract roads from drone images and then apply hyperparameter optimization to models with the highest performance. As a result, the LinkNet model using pre-trained ResNet-34 as the encoder achieved 85.125 mIoU. Subsequent studies should focus on comparing the results of this study with those of studies using state-of-the-art object detection algorithms or semi-supervised learning-based Semantic Segmentation techniques. The results of this study can be applied to improve the speed of the existing map update process.
Keywords
Drone Image; Semantic Segmentation; Remote Sensing; Road Extraction;
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Times Cited By KSCI : 3  (Citation Analysis)
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