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The Arctic coastline spans multiple countries, supports Indigenous livelihoods, and plays a vital role in the Arctic system. Rapid climate change is accelerating permafrost thaw, sea-level rise, and coastal erosion, underscoring the need for decision making to be informed by accurate delineation of tundra shoreline (instantaneous water line) and bluff edge (vegetation–slope boundary) position and change trends. To address this need, we compared two image segmentation approaches for mapping Arctic land and water interfaces from high-resolution satellite imagery. (1) U-Net, a supervised convolutional neural network trained on expert-annotated scenes, and (2) Differentiable Feature Clustering (DifFeat), an unsupervised model applied in a minimally supervised manner via expert-guided cluster selection. The shoreline and bluff edge boundaries were derived from the segmented land and water masks using an automated interface extraction approach. DifFeat achieved higher segmentation accuracy, with IoU values of 0.95 (water) and 0.92 (land), compared to U-Net’s 0.58 and 0.50, respectively. U-Net produced reliable results and benefited from infrared and vegetation spectral indices, but required extensive annotation and showed limited generalization to UAV imagery. DifFeat achieved superior results without manual annotation, reducing the dependence on labeled data and completing training 99.87% faster than U-Net. These findings highlight the complementary strengths of supervised and semi-supervised models for Arctic coastal mapping, with DifFeat offering a scalable, label-efficient solution for long-term coastal-change monitoring. Future work will integrate elevation data to further improve bluff edge feature detection. 

Precise coastal shoreline mapping is essential for monitoring changes in erosion rates, surface hydrology, and ecosystem structure and function. Monitoring water bodies in the Arctic National Wildlife Refuge (ANWR) is of high importance, especially considering the potential for oil and natural gas exploration in the region. In this work, we propose a modified variant of the Deep Neural Network based U-Net Architecture for the automated mapping of 4 Band Orthorectified NOAA Airborne Imagery using sparsely labeled training data and compare it to the performance of traditional Machine Learning (ML) based approaches—namely, random forest, xgboost—and spectral water indices—Normalized Difference Water Index (NDWI), and Normalized Difference Surface Water Index (NDSWI)—to support shoreline mapping of Arctic coastlines. We conclude that it is possible to modify the U-Net model to accept sparse labels as input and the results are comparable to other ML methods (an Intersection-over-Union (IoU) of 94.86% using U-Net vs. an IoU of 95.05% using the best performing method). 

This data set is associated with Curasi et al., 2022 (https://doi.org/10.1088/1748-9326/ac6005) it includes detailed survey data for Eriophorum vaginatum spanning sites in the Alaskan, Canadian, and Russian Arctic. The data set includes detailed surveys of tussock density and diameter, shrub basal diameter and abundance, the relationship between tussock size and mass, the relationship between shrub size and mass, tussock chemical properties, soil properties, and bulk density. It also includes outputs and projections from a tussock machine learning ecological niche model. This data was collected to characterize the distribution of this foundation species across the landscape and illustrate its role in the ecosystem. The data collection methods and subsequent analysis are described in detail in https://doi.org/10.1088/1748-9326/ac6005