| Summary: | We present Calving Front Machine (CALFIN), an automated method for extracting calving fronts from satellite images of marine-terminating glaciers. The results use Landsat imagery from 1972 to 2019 to generate 22,678 calving front lines across 66 Greenlandic glaciers. The method uses deep learning, and builds on existing work by Mohajerani et al., Zhang et al., and Baumhoer et al. Additional post-processing techniques allow for accurate segmentation of imagery into Shapefile outputs. This method is uniquely robust to the impact of clouds, illumination differences, ice mélange, and Landsat-7 Scan Line Corrector errors. CALFIN provides improvements on the current state of the art. A model inter-comparison is performed to evaluate performance against existing methodologies. CALFIN's ability to generalize to SAR imagery is also evaluated. CALFIN's fronts are often indistinguishable from manually-curated fronts, deviating by 2.25 pixels (86.76 meters) from the true front on a diverse set of 162 testing images. The current implementation offers a new opportunity to explore sub-seasonal trends on the extent of Greenland's margins, and supplies new constraints for simulations of the evolution of the mass balance of the Greenland Ice Sheet and its contributions to future sea level rise. We provide two levels of data products. Level 0 products consist of fjord boundary GeoTiff masks, the domain Shapefiles used for subsetting, a glacier names reference Shapefile, and the Landsat scene name ID lists. Level 1 product consists of a LineString Shapefile with 22678 features, and a Polygon Shapefile with 17771 features, grouped by glacial basin. Polygons are ocean masks that are constructed from merged calving front Line Strings, fjord boundaries, and domain boundaries. Both Shapefiles share a feature schema derived from the MEaSUREs glacial terminus positions dataset (NSIDC-0642) , as detailed in Table S2: Properties Spatial extent: 66 Greenlandic glacial basins, including Petermann, Upernavik, Rink Isbrae, Jakobshavn, ...
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