Coastal geomorphic classification identifying the type of depositional environment, is an important indicator for coastal state and vulnerability, e.g., beach, bedrock and wetland (tidal flat and estuarine/delta/back-barrier depression). Accurate and current maps of these geomorphology classes from local (100 m-1 km) to global (105 km) scales are important for a range of objectives in the coastal zone. Previous methods to create coastal geomorphic maps relied heavily on manual efforts and expert opinions. However, these approaches are labour intensive and impractical for consistent application at a global scale, while still including local details. Existing global coastal geomorphology datasets are a compilation of different local-scale datasets, with varying spatial, categorical resolutions and accuracies. The recent availability of “big” and “open” data as well as increased processing capabilities resulted in the use of satellite images and digital elevation models to identify sandy beach and cliff separately. In our work, these raster data were processed on Google Earth Engine, combined with shape descriptors of coastline vectors and then fitted into machine learning models to classify the intertidal coastal geomorphology into bedrock, beach or wetland classes on the global scale (from 56° S to 60° N, excluding coral reef/fringing reef and fjord dominated coasts). A unique attribute of this new method was the incorporation of shape descriptors extracted from shoreline vector data, which substantially improved the classification accuracy. Using existing data from European Union (EU), the United States of America (US) and Australia for training and testing, the model achieved averaged 85% accuracy for testing datasets in these continents and 84% accuracy for 10,000 independent global validation samples beyond these continents. Successful classification was achieved for coasts dominated by a single geomorphology class, as well as more complex environments, such as headland bay and barrier island systems, where multiple classes appear next to each other. The model had relatively less confidence in coasts with sandy cliffs, tall canopy cover and engineered structures. Complete statistics from this study covering 56° S to 60° N showed 36.8% (142,845 km) wetland, 26.7% (103,762 km) beach and 36.5% (141,579 km) bedrock. Beach and bedrock geomorphic classes were commonly detected at mid-latitudes (30o to 60o N/S), while wetland dominated tropical latitudes (0o to 30o N/S). The output dataset can be used for different coastal management purposes. The accuracy and categorical resolution of the classification can be further improved with the development of Earth Observation Big Data in the future.