Mapping the Margins: Evaluating Accuracy and Ambiguity in Automated Rock Glacier Delineation

Rock glaciers, the most visible surface expression of permafrost landforms, are found across glacial, periglacial and paraglacial environments. Accurate and consistent mapping of their extent is fundamental for advancing research in geomorphology, hydrology, ecology, geohazard assessment, permafrost dynamics, and climate studies. However, delineating their boundary remains challenging because rock glaciers often occur alongside or merge with other geomorphic equifinal landforms that are difficult to distinguish by their spectral identity in aerial or satellite imagery. Additionally, their boundaries are inherently ambiguous, evolving with changes in topographic and climatic factors. The widely used approach involving manual digitisation through visual interpretation of geomorphic features is time-consuming and subjective. Recent studies have explored deep learning as a means to automate and scale up rock glacier mapping, but existing studies still remain limited in number and geographic scope, with minimal attention to evaluating discrepancies or uncertainties in mapped extents. This study examines the use of a U-Net deep learning model for automated delineation of rock glacier extent, with particular emphasis on associated uncertainties. Using data from the Chile National Glacier Inventory for the Coquimbo region, we trained the model under two strategies: (1) differentiated training based on rock glacier types. A set of models was trained exclusively on landforms with clearly expressed geomorphological features of frontal slopes, lateral margin, and ridge-furrow structures, while another set incorporated all inventoried rock glaciers, including both well-expressed and subdued geomorphological features; (2) different predictor combinations, comparing a configuration that used only RGB + NIR bands from Sentinel 2 or PlanetScope imagery with an expanded set that integrated these spectral bands with DEM derivatives and imagery-derived variables. The highest-performing models from these strategies were then applied to an independent test area, and their outputs were compared against existing inventories to evaluate spatial consistency and assess potential mapping biases. By integrating an automated method with uncertainty assessment, this work contributes to the ongoing advancement of rock glacier detection and delineation methods and highlights the critical need to validate deep learning outputs. Such uncertainty quantification is essential for ensuring the robustness of mapped extents and for supporting applications that depend on accurate and reliable representations of landforms with inherently ambiguous and dynamic boundaries.