Academic Journal
Global road extraction using a pseudo-label guided framework: from benchmark dataset to cross-region semi-supervised learning
| العنوان: | Global road extraction using a pseudo-label guided framework: from benchmark dataset to cross-region semi-supervised learning |
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| المؤلفون: | Xiaoyan Lu, Yanfei Zhong, Zhuo Zheng, JunJue Wang, Dingyuan Chen, Yu Su |
| المصدر: | Geo-spatial Information Science, Pp 1-19 (2024) |
| بيانات النشر: | Taylor & Francis Group, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Mathematical geography. Cartography LCC:Geodesy |
| مصطلحات موضوعية: | Road extraction, global-scale, remote sensing, deep learning, semi-supervised learning, Mathematical geography. Cartography, GA1-1776, Geodesy, QB275-343 |
| الوصف: | Recent advancements in satellite remote sensing technology and computer vision have enabled rapid extraction of road networks from massive, Very High-Resolution (VHR) satellite imagery. However, current road extraction methods face the following limitations: 1) Insufficient availability of accurate and diverse training datasets for global-scale road extraction; 2) Costly and time-consuming manual labeling of millions of road samples; and 3) Limited generalization ability of deep learning models across diverse global contexts, resulting in better performance for regions well-represented in the training dataset, but worse performance when faced with domain gaps. To address these challenges, a semi-supervised framework was developed in this study, which includes a global-scale benchmark dataset – termed GlobalRoadSet (GRSet) – and a pseudo-label guided semi-supervised road extraction network – termed GlobalRoadNetSF (GRNetSF). The GRSet dataset was constructed using high-resolution satellite imagery and open-source crowdsourced OpenStreetMap (OSM) data. It comprises 47,210 samples collected from 121 capital cities across six populated continents. The GRNetSF trains the network by generating pseudo-labels for unlabeled images, combined with a few labeled samples from the target region. To enhance the quality of the pseudo-labels, strong data augmentation perturbation and auxiliary feature perturbation techniques are employed to ensure model prediction consistency. The proposed GRNetSF_GRSet framework was implemented in over 30 cities worldwide, where most of the Intersection-over-Union (IoU) values increased by more than 10%. This outcome confirms its strong generalization ability. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 10095020 1993-5153 1009-5020 |
| Relation: | https://doaj.org/toc/1009-5020; https://doaj.org/toc/1993-5153 |
| DOI: | 10.1080/10095020.2024.2362760 |
| URL الوصول: | https://doaj.org/article/d4538b27351d407885e29da57b1cae50 |
| رقم الانضمام: | edsdoj.4538b27351d407885e29da57b1cae50 |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 10095020 19935153 |
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| DOI: | 10.1080/10095020.2024.2362760 |