Improved Road Extraction Models through Semi-Supervised Learning with ACCT
Improving the performance and reducing the training cost of road extraction models in the absence of samples is important for updating road maps. Despite the success of recent road extraction models on standard datasets, they often fail to perform when applied to new datasets or real-world scenarios...
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MDPI AG
2024-09-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/13/10/347 |
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| author | Hao Yu Shihong Du Zhenshan Tan Xiuyuan Zhang Zhijiang Li |
| author_facet | Hao Yu Shihong Du Zhenshan Tan Xiuyuan Zhang Zhijiang Li |
| author_sort | Hao Yu |
| collection | DOAJ |
| description | Improving the performance and reducing the training cost of road extraction models in the absence of samples is important for updating road maps. Despite the success of recent road extraction models on standard datasets, they often fail to perform when applied to new datasets or real-world scenarios where labeled samples are not available. In this paper, our focus diverges from the typical quest to pinpoint the optimal road extraction model or evaluate generalization prowess across models. Instead, we propose a method called Asymmetric Consistent Co-Training (ACCT) to train existing road extraction models faster and make them perform better in new scenarios lacking samples. ACCT uses two models with different structures and a supervision module to enhance accuracy through mutual learning. Labeled and unlabeled images are processed by both models to generate road maps from different perspectives. The supervision module ensures consistency between predictions by computing losses based on labeling status. ACCT iteratively adjusts parameters using unlabeled data, improving generalization. Empirical evaluations show that ACCT improves IoU by 2.79% to 10.26% using only 1/8 of the labeled data compared to fully supervised methods. It also reduces parameters by over 49% compared to state-of-the-art semi-supervised methods while maintaining similar accuracy. These results highlight the potential of leveraging large amounts of unlabeled data to enhance road extraction models as data acquisition technology advances. |
| format | Article |
| id | doaj-art-b6be41b8c65044beaeafc59d83ee30d8 |
| institution | OA Journals |
| issn | 2220-9964 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-b6be41b8c65044beaeafc59d83ee30d82025-08-20T02:11:04ZengMDPI AGISPRS International Journal of Geo-Information2220-99642024-09-01131034710.3390/ijgi13100347Improved Road Extraction Models through Semi-Supervised Learning with ACCTHao Yu0Shihong Du1Zhenshan Tan2Xiuyuan Zhang3Zhijiang Li4School of Earth and Space Sciences, Peking University, Beijing 100871, ChinaCollege of Urban and Environmental Sciences, Peking University, Beijing 100871, ChinaSchool of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollege of Urban and Environmental Sciences, Peking University, Beijing 100871, ChinaSchool of Information Management, Wuhan University, Wuhan 430072, ChinaImproving the performance and reducing the training cost of road extraction models in the absence of samples is important for updating road maps. Despite the success of recent road extraction models on standard datasets, they often fail to perform when applied to new datasets or real-world scenarios where labeled samples are not available. In this paper, our focus diverges from the typical quest to pinpoint the optimal road extraction model or evaluate generalization prowess across models. Instead, we propose a method called Asymmetric Consistent Co-Training (ACCT) to train existing road extraction models faster and make them perform better in new scenarios lacking samples. ACCT uses two models with different structures and a supervision module to enhance accuracy through mutual learning. Labeled and unlabeled images are processed by both models to generate road maps from different perspectives. The supervision module ensures consistency between predictions by computing losses based on labeling status. ACCT iteratively adjusts parameters using unlabeled data, improving generalization. Empirical evaluations show that ACCT improves IoU by 2.79% to 10.26% using only 1/8 of the labeled data compared to fully supervised methods. It also reduces parameters by over 49% compared to state-of-the-art semi-supervised methods while maintaining similar accuracy. These results highlight the potential of leveraging large amounts of unlabeled data to enhance road extraction models as data acquisition technology advances.https://www.mdpi.com/2220-9964/13/10/347road extractionsemi-supervised learningfeature learningtraining cost |
| spellingShingle | Hao Yu Shihong Du Zhenshan Tan Xiuyuan Zhang Zhijiang Li Improved Road Extraction Models through Semi-Supervised Learning with ACCT ISPRS International Journal of Geo-Information road extraction semi-supervised learning feature learning training cost |
| title | Improved Road Extraction Models through Semi-Supervised Learning with ACCT |
| title_full | Improved Road Extraction Models through Semi-Supervised Learning with ACCT |
| title_fullStr | Improved Road Extraction Models through Semi-Supervised Learning with ACCT |
| title_full_unstemmed | Improved Road Extraction Models through Semi-Supervised Learning with ACCT |
| title_short | Improved Road Extraction Models through Semi-Supervised Learning with ACCT |
| title_sort | improved road extraction models through semi supervised learning with acct |
| topic | road extraction semi-supervised learning feature learning training cost |
| url | https://www.mdpi.com/2220-9964/13/10/347 |
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