Journal Article

GCL_(F)CS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020

Wang, Yang, Zuo, Jian, Xiao, Jingfeng, Li, Kaixin, Zhang, Li, Zhang, Bo, Hu, Yingwen, Chen, Bowei, Mamun, M. M. Abdullah Al

2025

Overview

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.

• Publisher: Nature Publishing Group UK
• Subject: Data Descriptor