Bedforms are key components of Earth surfaces and yet their evaluation typically relies on manual measurements that are challenging to reproduce. Several methods exist to automate their identification and calculate their metrics, but they often exhibit limitations where applied at large scales. This paper presents an innovative workflow for identifying and measuring individual depositional bedforms. The workflow relies on the identification of local minima and maxima that are grouped by neighbourhood analysis and calibrated using curvature. The method was trialed using a synthetic digital elevation model and two bathymetry surveys from Australia’s northwest marine region, resulting in the identification of nearly 2000 bedforms. The comparison of the metrics calculated for each individual feature with manual measurements show differences of less than 10%, indicating the robustness of the workflow. The cross-comparison of the metrics resulted in the definition of several sub-types of bedforms, including sandwaves and palaeoshorelines, that were then correlated with oceanic conditions, further corroborating the validity of the workflow. Results from this study support the idea that the use of automated methods to characterise bedforms should be further developed and that the integration of automated measurements at large scales will support the development of new classification charts that currently rely
solely on manual measurements.