{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:25:59Z","timestamp":1776129959663,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,1,3]],"date-time":"2019-01-03T00:00:00Z","timestamp":1546473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The continuous and precise mapping of glacier calving fronts is essential for monitoring and understanding rapid glacier changes in Antarctica and Greenland, which have the potential for significant sea level rise within the current century. This effort has been mostly restricted to the slow and painstaking manual digitalization of the calving front positions in thousands of satellite imagery products. Here, we have developed a machine learning toolkit to automatically detect glacier calving front margins in satellite imagery. The toolkit is based on semantic image segmentation using Convolutional Neural Networks (CNN) with a modified U-Net architecture to isolate the calving fronts from satellite images after having been trained with a dataset of images and their corresponding manually-determined calving fronts. As a case study we train our neural network on a varied set of Landsat images with lowered resolutions from Jakobshavn, Sverdrup, and Kangerlussuaq glaciers, Greenland and test the results on images from Helheim glacier, Greenland to evaluate the performance of the approach. The neural network is able to identify the calving front in new images with a mean deviation of 96.3 m from the true fronts, equivalent to 1.97 pixels on average, while the corresponding error for manually-determined fronts on the same resolution images is 92.5 m (1.89 pixels). We find that the trained neural network significantly outperforms common edge detection techniques, and can be used to continuously map out calving-ice fronts with a variety of data products.<\/jats:p>","DOI":"10.3390\/rs11010074","type":"journal-article","created":{"date-parts":[[2019,1,3]],"date-time":"2019-01-03T11:11:56Z","timestamp":1546513916000},"page":"74","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":77,"title":["Detection of Glacier Calving Margins with Convolutional Neural Networks: A Case Study"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4292-2367","authenticated-orcid":false,"given":"Yara","family":"Mohajerani","sequence":"first","affiliation":[{"name":"Earth System Science, University of California, Irvine, CA 92617, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3074-7845","authenticated-orcid":false,"given":"Michael","family":"Wood","sequence":"additional","affiliation":[{"name":"Earth System Science, University of California, Irvine, CA 92617, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9020-1898","authenticated-orcid":false,"given":"Isabella","family":"Velicogna","sequence":"additional","affiliation":[{"name":"Earth System Science, University of California, Irvine, CA 92617, USA"},{"name":"Jet Propulsion Laboratory, Pasadena, CA 91109, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3366-0481","authenticated-orcid":false,"given":"Eric","family":"Rignot","sequence":"additional","affiliation":[{"name":"Earth System Science, University of California, Irvine, CA 92617, USA"},{"name":"Jet Propulsion Laboratory, Pasadena, CA 91109, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Howat, I.M., Joughin, I., Tulaczyk, S., and Gogineni, S. (2005). Rapid retreat and acceleration of Helheim Glacier, east Greenland. Geophys. Res. Lett., 32.","DOI":"10.1029\/2005GL024737"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Moon, T., and Joughin, I. (2008). Changes in ice front position on Greenland\u2019s outlet glaciers from 1992 to 2007. J. Geophys. Res. Earth Surf., 113.","DOI":"10.1029\/2007JF000927"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Seale, A., Christoffersen, P., Mugford, R.I., and O\u2019Leary, M. (2011). Ocean forcing of the Greenland Ice Sheet: Calving fronts and patterns of retreat identified by automatic satellite monitoring of eastern outlet glaciers. J. Geophys. Res. Earth Surf., 116.","DOI":"10.1029\/2010JF001847"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1038\/ngeo1481","article-title":"An aerial view of 80 years of climate-related glacier fluctuations in southeast Greenland","volume":"5","author":"Korsgaard","year":"2012","journal-title":"Nat. Geosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1657\/AAAR0014-049","article-title":"Extensive retreat of Greenland tidewater glaciers, 2000\u20132010","volume":"47","author":"Murray","year":"2015","journal-title":"Arct. Antarct. Alpine Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8334","DOI":"10.1029\/2018GL078024","article-title":"Ocean-induced melt triggers glacier retreat in Northwest Greenland","volume":"45","author":"Wood","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6374","DOI":"10.1002\/2016GL068784","article-title":"Modeling of ocean-induced ice melt rates of five west Greenland glaciers over the past two decades","volume":"43","author":"Rignot","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"759","DOI":"10.3189\/2013JoG12J238","article-title":"Seasonal variations of outlet glacier terminus position in Greenland","volume":"59","author":"Schild","year":"2013","journal-title":"J. Glaciol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1017\/jog.2016.138","article-title":"Asynchronous behavior of outlet glaciers feeding Godth\u00e5bsfjord (Nuup Kangerlua) and the triggering of Narsap Sermia\u2019s retreat in SW Greenland","volume":"63","author":"Motyka","year":"2017","journal-title":"J. Glaciol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2024","DOI":"10.1029\/2017JF004499","article-title":"Geometric controls on tidewater glacier retreat in central western Greenland","volume":"123","author":"Catania","year":"2018","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1029\/2018JF004628","article-title":"Reconciling drivers of seasonal terminus advance and retreat at 13 Central West Greenland tidewater glaciers","volume":"123","author":"Fried","year":"2018","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1002\/2015JF003494","article-title":"Seasonal to multiyear variability of glacier surface velocity, terminus position, and sea ice\/ice m\u00e9lange in northwest Greenland","volume":"120","author":"Moon","year":"2015","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.actaastro.2017.07.023","article-title":"Design and implementation of a Cube satellite mission for Antarctic glacier and sea ice observation","volume":"139","author":"Wu","year":"2017","journal-title":"Acta Astronaut."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_15","unstructured":"Potin, P., Rosich, B., Miranda, N., and Grimont, P. (2018, January 4\u20137). Sentinel-1A\/-1B Mission Status. Proceedings of the EUSAR 2018: 12th European Conference on Synthetic Aperture Radar, Aachen, Germany."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.rse.2016.12.029","article-title":"The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): Science requirements, concept, and implementation","volume":"190","author":"Markus","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/0031-3203(81)90028-5","article-title":"A survey on image segmentation","volume":"13","author":"Fu","year":"1981","journal-title":"Pattern Recognit."},{"key":"ref_18","unstructured":"Sobel, I. (1990). An isotropic 3 \u00d7 3 image gradient operator. Machine Vision for Three-Dimensional Scenes, Academic Press, Inc."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Witkin, A., Terzopoulos, D., and Kass, M. (1987). Signal matching through scale space. Readings in Computer Vision, Elsevier.","DOI":"10.1016\/B978-0-08-051581-6.50073-8"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1109\/34.56205","article-title":"Scale-space and edge detection using anisotropic diffusion","volume":"12","author":"Perona","year":"1990","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1029\/97RG03139","article-title":"Radar interferometry and its application to changes in the Earth\u2019s surface","volume":"36","author":"Massonnet","year":"1998","journal-title":"Rev. Geophys."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_23","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","article-title":"Deep convolutional neural networks for image classification: A comprehensive review","volume":"29","author":"Rawat","year":"2017","journal-title":"Neural Comput."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nagi, J., Ducatelle, F., Di Caro, G.A., Cire\u015fan, D., Meier, U., Giusti, A., Nagi, F., Schmidhuber, J., and Gambardella, L.M. (2011, January 16\u201318). Max-pooling convolutional neural networks for vision-based hand gesture recognition. Proceedings of the 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICSIPA.2011.6144164"},{"key":"ref_29","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_30","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Mannor, S., Peleg, D., and Rubinstein, R. (2005, January 7\u201311). The cross entropy method for classification. Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany.","DOI":"10.1145\/1102351.1102422"},{"key":"ref_32","unstructured":"Kingma, D.P., and Ba, J. (arXiv, 2014). Adam: A method for stochastic optimization, arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/1\/74\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:23:23Z","timestamp":1760185403000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/1\/74"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,3]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["rs11010074"],"URL":"https:\/\/doi.org\/10.3390\/rs11010074","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints201811.0529.v1","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,3]]}}}