{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:00:53Z","timestamp":1760234453282,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,15]],"date-time":"2021-05-15T00:00:00Z","timestamp":1621036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"E2S UPPA","award":["BIGCEES"],"award-info":[{"award-number":["BIGCEES"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Data about storm impacts are essential for the disaster risk reduction process, but unlike data about storm characteristics, they are not routinely collected. In this paper, we demonstrate the high potential of convolutional neural networks to automatically constitute storm impact database using timestacks images provided by coastal video monitoring stations. Several convolutional neural network architectures and methods to deal with class imbalance were tested on two sites (Biarritz and Zarautz) to find the best practices for this classification task. This study shows that convolutional neural networks are well adapted for the classification of timestacks images into storm impact regimes. Overall, the most complex and deepest architectures yield better results. Indeed, the best performances are obtained with the VGG16 architecture for both sites with F-scores of 0.866 for Biarritz and 0.858 for Zarautz. For the class imbalance problem, the method of oversampling shows best classification accuracy with F-scores on average 30% higher than the ones obtained with cost sensitive learning. The transferability of the learning method between sites is also investigated and shows conclusive results. This study highlights the high potential of convolutional neural networks to enhance the value of coastal video monitoring data that are routinely recorded on many coastal sites. Furthermore, it shows that this type of deep neural network can significantly contribute to the setting up of risk databases necessary for the determination of storm risk indicators and, more broadly, for the optimization of risk-mitigation measures.<\/jats:p>","DOI":"10.3390\/rs13101933","type":"journal-article","created":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T02:31:34Z","timestamp":1621218694000},"page":"1933","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Automatic Creation of Storm Impact Database Based on Video Monitoring and Convolutional Neural Networks"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1178-8393","authenticated-orcid":false,"given":"Aurelien","family":"Callens","sequence":"first","affiliation":[{"name":"LMAP, Universit\u00e9 de Pau et des Pays de l\u2019Adour, E2S UPPA, CNRS, 64000 Pau, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4264-0412","authenticated-orcid":false,"given":"Denis","family":"Morichon","sequence":"additional","affiliation":[{"name":"SIAME, Universit\u00e9 de Pau et des Pays de l\u2019Adour, E2S UPPA, 64600 Anglet, France"}]},{"given":"Pedro","family":"Liria","sequence":"additional","affiliation":[{"name":"AZTI, Marine Research Division, KOSTARISK, 20110 Pasaia, Spain"}]},{"given":"Irati","family":"Epelde","sequence":"additional","affiliation":[{"name":"AZTI, Marine Research Division, KOSTARISK, 20110 Pasaia, Spain"}]},{"given":"Benoit","family":"Liquet","sequence":"additional","affiliation":[{"name":"LMAP, Universit\u00e9 de Pau et des Pays de l\u2019Adour, E2S UPPA, CNRS, 64000 Pau, France"},{"name":"Department of Mathematics and Statistics, Macquarie University, Sydney, NSW 2006, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,15]]},"reference":[{"key":"ref_1","first-page":"977","article-title":"Prototype of Early Warning System for Coastal Storm Hazard (Bulgarian Black Sea Coast)","volume":"67","author":"Valchev","year":"2014","journal-title":"CR Acad. 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