{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T08:30:47Z","timestamp":1790757047325,"version":"4.1.0"},"reference-count":53,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,8]],"date-time":"2020-11-08T00:00:00Z","timestamp":1604793600000},"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>This article proposes a new methodological approach to measure and map the size of coarse clasts on a land surface from photographs. This method is based on the use of the Mask Regional Convolutional Neural Network (R-CNN) deep learning algorithm, which allows the instance segmentation of objects after an initial training on manually labeled data. The algorithm is capable of identifying and classifying objects present in an image at the pixel scale, without human intervention, in a matter of seconds. This work demonstrates that it is possible to train the model to detect non-overlapping coarse sediments on scaled images, in order to extract their individual size and morphological characteristics with high efficiency (R2 = 0.98; Root Mean Square Error (RMSE) = 3.9 mm). It is then possible to measure element size profiles over a sedimentary body, as it was done on the pebble beach of Etretat (Normandy, France) in order to monitor the granulometric spatial variability before and after a storm. Applied at a larger scale using Unmanned Aerial Vehicle (UAV) derived ortho-images, the method allows the accurate characterization and high-resolution mapping of the surface coarse sediment size, as it was performed on the two pebble beaches of Etretat (D50 = 5.99 cm) and Hautot-sur-Mer (D50 = 7.44 cm) (Normandy, France). Validation results show a very satisfying overall representativity (R2 = 0.45 and 0.75; RMSE = 6.8 mm and 9.3 mm at Etretat and Hautot-sur-Mer, respectively), while the method remains fast, easy to apply and low-cost, although the method remains limited by the image resolution (objects need to be longer than 4 cm), and could still be improved in several ways, for instance by adding more manually labeled data to the training dataset, and by considering more accurate methods than the ellipse fitting for measuring the particle sizes.<\/jats:p>","DOI":"10.3390\/rs12213659","type":"journal-article","created":{"date-parts":[[2020,11,8]],"date-time":"2020-11-08T19:03:37Z","timestamp":1604862217000},"page":"3659","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["A Deep Learning-Based Method for Quantifying and Mapping the Grain Size on Pebble Beaches"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8597-2858","authenticated-orcid":false,"given":"Antoine","family":"Soloy","sequence":"first","affiliation":[{"name":"Continental and Coastal Morphodynamics Laboratory (M2C), Department of Geosciences, University of Rouen Normandy, CNRS UMR 6143, Place Emile 10 Blondel, 76130 Mont Saint Aignan, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Imen","family":"Turki","sequence":"additional","affiliation":[{"name":"Continental and Coastal Morphodynamics Laboratory (M2C), Department of Geosciences, University of Rouen Normandy, CNRS UMR 6143, Place Emile 10 Blondel, 76130 Mont Saint Aignan, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4119-5399","authenticated-orcid":false,"given":"Matthieu","family":"Fournier","sequence":"additional","affiliation":[{"name":"Continental and Coastal Morphodynamics Laboratory (M2C), Department of Geosciences, University of Rouen Normandy, CNRS UMR 6143, Place Emile 10 Blondel, 76130 Mont Saint Aignan, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"St\u00e9phane","family":"Costa","sequence":"additional","affiliation":[{"name":"Shoreline\u2014Environment\u2014Remote Sensing\u2014Geomatics Laboratory (LETG), Department of Physical Geography and Environment, University of Caen Normandy, CNRS, UMR 6554, 12 Esplanade de la Paix, 14000 Caen, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bastien","family":"Peuziat","sequence":"additional","affiliation":[{"name":"Shoreline\u2014Environment\u2014Remote Sensing\u2014Geomatics Laboratory (LETG), Department of Physical Geography and Environment, University of Caen Normandy, CNRS, UMR 6554, 12 Esplanade de la Paix, 14000 Caen, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Lecoq","sequence":"additional","affiliation":[{"name":"Continental and Coastal Morphodynamics Laboratory (M2C), Department of Geosciences, University of Rouen Normandy, CNRS UMR 6143, Place Emile 10 Blondel, 76130 Mont Saint Aignan, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.earscirev.2006.06.003","article-title":"Concepts in gravel beach dynamics","volume":"79","author":"Buscombe","year":"2006","journal-title":"Earth-Science Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"166","DOI":"10.2112\/1551-5036(2004)20[166:CCSATC]2.0.CO;2","article-title":"Coastal classification: Systematic approaches to consider in the development of a comprehensive scheme","volume":"20","author":"Finkl","year":"2004","journal-title":"J. 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