City-scale industrial tank detection using multi-source spatial data fusion
This paper focuses on the automatic detection of industrial storage tanks in urban areas using deep learning-based algorithms. Industrial storage tanks are critical for storing raw materials, finished products, and intermediate products in industries such as petroleum, chemical, and metallurgy. Howe...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2433615 |
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| _version_ | 1846150989929775104 |
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| author | Zhibao Wang Mingyuan Zhu Lu Bai Jinhua Tao Mei Wang Xiaoqing He Anna Jurek-Loughrey Liangfu Chen |
| author_facet | Zhibao Wang Mingyuan Zhu Lu Bai Jinhua Tao Mei Wang Xiaoqing He Anna Jurek-Loughrey Liangfu Chen |
| author_sort | Zhibao Wang |
| collection | DOAJ |
| description | This paper focuses on the automatic detection of industrial storage tanks in urban areas using deep learning-based algorithms. Industrial storage tanks are critical for storing raw materials, finished products, and intermediate products in industries such as petroleum, chemical, and metallurgy. However, they can leak and cause environmental damage, making it important to monitor them in cities. The challenge lies in the large number and dispersed distribution of these tanks throughout the city. To address this, high-resolution remote sensing images and deep learning algorithms are used to improve the accuracy of industrial storage tank detection at the city scale. We construct a city scale industrial storage tank object detection dataset using high-resolution remote sensing images and explore the effect of deep learning object detection algorithm optimization on industrial storage tank detection. Techniques including ResNet50, FPN, and dilated convolution are utilized in this work for improving the model detection accuracy. Furthermore, we construct multiple city industrial storage tank datasets based on high-resolution remote sensing images enriched by integrating point-of-interest data and land use data of each city. The integration from multiple data sources provides an accurate and efficient solution for the identification of industrial storage tanks within urban areas. |
| format | Article |
| id | doaj-art-d94a3d65b3f246c9a551a9417cfd7adf |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-d94a3d65b3f246c9a551a9417cfd7adf2024-11-28T04:49:51ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552024-12-0117110.1080/17538947.2024.2433615City-scale industrial tank detection using multi-source spatial data fusionZhibao Wang0Mingyuan Zhu1Lu Bai2Jinhua Tao3Mei Wang4Xiaoqing He5Anna Jurek-Loughrey6Liangfu Chen7Bohai Rim Energy Research Institute, Northeast Petroleum University, Qinhuangdao, People’s Republic of ChinaSchool of Computer and Information Technology, Northeast Petroleum University, Daqing, People’s Republic of ChinaSchool of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, UKState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences Beijing Normal University, Beijing, People’s Republic of ChinaSchool of Computer and Information Technology, Northeast Petroleum University, Daqing, People’s Republic of ChinaSchool of Computer and Information Technology, Northeast Petroleum University, Daqing, People’s Republic of ChinaSchool of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, UKState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences Beijing Normal University, Beijing, People’s Republic of ChinaThis paper focuses on the automatic detection of industrial storage tanks in urban areas using deep learning-based algorithms. Industrial storage tanks are critical for storing raw materials, finished products, and intermediate products in industries such as petroleum, chemical, and metallurgy. However, they can leak and cause environmental damage, making it important to monitor them in cities. The challenge lies in the large number and dispersed distribution of these tanks throughout the city. To address this, high-resolution remote sensing images and deep learning algorithms are used to improve the accuracy of industrial storage tank detection at the city scale. We construct a city scale industrial storage tank object detection dataset using high-resolution remote sensing images and explore the effect of deep learning object detection algorithm optimization on industrial storage tank detection. Techniques including ResNet50, FPN, and dilated convolution are utilized in this work for improving the model detection accuracy. Furthermore, we construct multiple city industrial storage tank datasets based on high-resolution remote sensing images enriched by integrating point-of-interest data and land use data of each city. The integration from multiple data sources provides an accurate and efficient solution for the identification of industrial storage tanks within urban areas.https://www.tandfonline.com/doi/10.1080/17538947.2024.2433615Remote sensingindustrial storage tankmulti-source data fusionobject detection |
| spellingShingle | Zhibao Wang Mingyuan Zhu Lu Bai Jinhua Tao Mei Wang Xiaoqing He Anna Jurek-Loughrey Liangfu Chen City-scale industrial tank detection using multi-source spatial data fusion International Journal of Digital Earth Remote sensing industrial storage tank multi-source data fusion object detection |
| title | City-scale industrial tank detection using multi-source spatial data fusion |
| title_full | City-scale industrial tank detection using multi-source spatial data fusion |
| title_fullStr | City-scale industrial tank detection using multi-source spatial data fusion |
| title_full_unstemmed | City-scale industrial tank detection using multi-source spatial data fusion |
| title_short | City-scale industrial tank detection using multi-source spatial data fusion |
| title_sort | city scale industrial tank detection using multi source spatial data fusion |
| topic | Remote sensing industrial storage tank multi-source data fusion object detection |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2024.2433615 |
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