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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 (GCLFCS30) 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 GCLFCS30 offers significant advantages in capturing artificial coastlines, reflecting strong alignment with location validation data. The GCLFCS30 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 GCLFCS30 is the first global coastline category dataset covering the high latitudes in a continuous and smooth line vector format.
Zuo et al. (Wed,) studied this question.