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A method for extracting road attribute information from remote sensing images based on multi-task learning and its application in the periphery of nuclear power plants
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Tiehuan SU1, 2, 3, Kai QIN1, 2, 3, Yingjun ZHAO1, 2, 3, Zijia AN4, Yuxi HAO1, 2, 3
World Nuclear Geoscience | 2025, 42(2) : 374 - 384
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World Nuclear Geoscience | 2025, 42(2): 374-384
RESEARCH ARTICALS
A method for extracting road attribute information from remote sensing images based on multi-task learning and its application in the periphery of nuclear power plants
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Tiehuan SU1, 2, 3, Kai QIN1, 2, 3, Yingjun ZHAO1, 2, 3, Zijia AN4, Yuxi HAO1, 2, 3
Affiliations
  • 1 National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, Beijing 100029, China
  • 2 Beijing Research Institute of Uranium Geology, Beijing 100029, China
  • 3 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
Published: 2025-04-08 doi: 10.3969/j.issn.1672-0636.2025.02.012
Outline
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Roads,as typical man-made objects,have attracted considerable attention in the field of remote sensing. Previous research has predominantly focused on geometrical feature extraction,with relatively insufficient attention paid to road attribute information such as material, classification, and surrounding features. However,road attribute information is crucial for road management,urban planning,and more. Considering the inherent engineering and geographical relationships among these road attributes,this study adopts a multi-task learning approach. We propose a method for extracting road attributes from visible remote sensing images based on multi-task learning,utilizing a residual network integrated with a channel attention module as the backbone. This is further enhanced with a foreground auxiliary module and a feature pyramid module to augment the focus on road targets and the capability for multi-scale processing. Ultimately,the study achieves the classification of road material,classification,and surrounding feature types (background) in visible remote sensing images. and proved the overall accuracy of the network,demonstrating that convolutional networks can effectively extract features and learn engineering and geographical relationships. In the application to the periphery of a nuclear power plants,this method addressed the complex environment and strategic importance of nuclear facilities,validating its effectiveness in practical scenarios,which is of significant importance for ensuring the safe operation of nuclear power plants and the rational planning of surrounding areas.

multi-task machine learning  /  high-resolution remote sensing images  /  remote sensing image classification  /  road material  /  road classification
Tiehuan SU, Kai QIN, Yingjun ZHAO, Zijia AN, Yuxi HAO. A method for extracting road attribute information from remote sensing images based on multi-task learning and its application in the periphery of nuclear power plants[J]. World Nuclear Geoscience, 2025 , 42 (2) : 374 -384 . DOI: 10.3969/j.issn.1672-0636.2025.02.012
  • National Natural Science Foundation of China(41602333)
Year 2025 volume 42 Issue 2
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Article Info
doi: 10.3969/j.issn.1672-0636.2025.02.012
  • Receive Date:2025-02-14
  • Online Date:2025-10-29
  • Published:2025-04-08
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History
  • Received:2025-02-14
  • Revised:2025-03-03
Funding
National Natural Science Foundation of China(41602333)
Affiliations
    1 National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, Beijing 100029, China
    2 Beijing Research Institute of Uranium Geology, Beijing 100029, China
    3 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
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占总种数比例
Percentage of
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种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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