GCL_FCS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020
Abstract The coastline reflects coastal environmental processes and dynamic changes, serving as a fundamental parameter for coast. Although several global coastline datasets have been developed, they mainly focus on coastal morphology, the typology of coastlines are still lacking. We produced a Glob...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04430-0 |
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| author | Jian Zuo Li Zhang Jingfeng Xiao Bowei Chen Bo Zhang Yingwen Hu M. M. Abdullah Al Mamun Yang Wang Kaixin Li |
| author_facet | Jian Zuo Li Zhang Jingfeng Xiao Bowei Chen Bo Zhang Yingwen Hu M. M. Abdullah Al Mamun Yang Wang Kaixin Li |
| author_sort | Jian Zuo |
| collection | DOAJ |
| description | Abstract The coastline reflects coastal environmental processes and dynamic changes, serving as a fundamental parameter for coast. Although several global coastline datasets have been developed, they mainly focus on coastal morphology, the typology of coastlines are still lacking. We produced a Global CoastLine Dataset (GCL_FCS30) with a detailed classification system. The coastline extraction employed a combined algorithm incorporating the Modified Normalized Difference Water Index and an adaptive threshold segmentation method. The coastline classification was performed a hybrid transect classifier that integrates a random forest algorithm with stable training samples derived from multi-source geophysical data. The GCL_FCS30 offers significant advantages in capturing artificial coastlines, reflecting strong alignment with location validation data. The GCL_FCS30 classification was found to achieve an overall accuracy and Kappa coefficient over 85% and 0.75. Each coastline category accurately covered the majority of the area represented in third-party data and exhibited a high degree of spatial relevance. Therefore, the GCL_FCS30 is the first global coastline category dataset covering the high latitudes in a continuous and smooth line vector format. |
| format | Article |
| id | doaj-art-9698f69dc5434a959078cd0dcdef381f |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-9698f69dc5434a959078cd0dcdef381f2025-01-26T12:14:50ZengNature PortfolioScientific Data2052-44632025-01-0112112010.1038/s41597-025-04430-0GCL_FCS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020Jian Zuo0Li Zhang1Jingfeng Xiao2Bowei Chen3Bo Zhang4Yingwen Hu5M. M. Abdullah Al Mamun6Yang Wang7Kaixin Li8International Research Center of Big Data for Sustainable Development GoalsInternational Research Center of Big Data for Sustainable Development GoalsEarth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New HampshireInternational Research Center of Big Data for Sustainable Development GoalsInternational Research Center of Big Data for Sustainable Development GoalsInternational Research Center of Big Data for Sustainable Development GoalsInternational Research Center of Big Data for Sustainable Development GoalsInternational Research Center of Big Data for Sustainable Development GoalsInternational Research Center of Big Data for Sustainable Development GoalsAbstract The coastline reflects coastal environmental processes and dynamic changes, serving as a fundamental parameter for coast. Although several global coastline datasets have been developed, they mainly focus on coastal morphology, the typology of coastlines are still lacking. We produced a Global CoastLine Dataset (GCL_FCS30) with a detailed classification system. The coastline extraction employed a combined algorithm incorporating the Modified Normalized Difference Water Index and an adaptive threshold segmentation method. The coastline classification was performed a hybrid transect classifier that integrates a random forest algorithm with stable training samples derived from multi-source geophysical data. The GCL_FCS30 offers significant advantages in capturing artificial coastlines, reflecting strong alignment with location validation data. The GCL_FCS30 classification was found to achieve an overall accuracy and Kappa coefficient over 85% and 0.75. Each coastline category accurately covered the majority of the area represented in third-party data and exhibited a high degree of spatial relevance. Therefore, the GCL_FCS30 is the first global coastline category dataset covering the high latitudes in a continuous and smooth line vector format.https://doi.org/10.1038/s41597-025-04430-0 |
| spellingShingle | Jian Zuo Li Zhang Jingfeng Xiao Bowei Chen Bo Zhang Yingwen Hu M. M. Abdullah Al Mamun Yang Wang Kaixin Li GCL_FCS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020 Scientific Data |
| title | GCL_FCS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020 |
| title_full | GCL_FCS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020 |
| title_fullStr | GCL_FCS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020 |
| title_full_unstemmed | GCL_FCS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020 |
| title_short | GCL_FCS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020 |
| title_sort | gcl fcs30 a global coastline dataset with 30 m resolution and a fine classification system from 2010 to 2020 |
| url | https://doi.org/10.1038/s41597-025-04430-0 |
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