Automatic identification of streamlined subglacial bedforms using machine learning: an open‐source Python approach

Subglacial processes exert a major control on ice streaming. Constraining subglacial conditions thus allows for more accurate predictions of ice mass loss. Due to the difficulty in observing large‐scale conditions of the modern subglacial environment, we turn to geological records of ice streaming in deglaciated environments. Morphometric values of streamlined subglacial bedforms provide valuable information about the relative speed, direction, and maturity of past ice streams and the relationship between ice streaming and subglacial erosion and deposition. However, manually identifying streamlined subglacial bedforms across deglaciated landscapes, sometimes in clusters of several thousand, is an arduous task with difficult‐to‐control sources of variability and human‐biased errors. This paper presents a new tool that utilizes a machine learning approach to automatically identify glacially derived streamlined features. Slope variations across a landscape, identified by topographic position index, undergo analysis from a series of supervised machine learning models trained from over 600 000 data points identified across the deglaciated Northern Hemisphere. A filtered data set produced through the combination of scientifically driven preprocessing and statistical downsampling improved the robustness of our approach. After cross‐validation, we found that Random Forest detected the most true positives, up to 94.5% on a withheld test set, and an ensemble average of machine learning models provided the highest stability when applied within the range of applicable data sets, performing at up to 79% identification of true positives on an out of distribution area of interest. We build these models into an open‐source Python package, bedfinder, and apply it to new data in the Green Bay Lobe region, USA, finding the general ice‐flow direction and average streamlined subglacial bedform elongation with minimal effort. This type of open, reproducible machine learning analysis is at the leading edge of glacial geomorphology research and will continue to improve with integration of newly acquired and previously collected data.