{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T09:04:11Z","timestamp":1777971851722,"version":"3.51.4"},"reference-count":67,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T00:00:00Z","timestamp":1655856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Resources Canada through the Climate Change Geoscience Program","award":["773421"],"award-info":[{"award-number":["773421"]}]},{"name":"Polar Continental Shelf Project (PCSP)","award":["773421"],"award-info":[{"award-number":["773421"]}]},{"name":"Inuvialuit Regional Corporation (IRC)","award":["773421"],"award-info":[{"award-number":["773421"]}]},{"name":"Crown-Indigenous Relations and Northern Affairs Canada (CIRNAC)","award":["773421"],"award-info":[{"award-number":["773421"]}]},{"name":"European Union\u2019s Horizon 2020 Research and Innovation Programme","award":["773421"],"award-info":[{"award-number":["773421"]}]},{"name":"NSERC PermafrostNet strategic network and a NSERC Discovery Grant","award":["773421"],"award-info":[{"award-number":["773421"]}]},{"name":"Northern Supplement to B.M.","award":["773421"],"award-info":[{"award-number":["773421"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Permafrost coasts are experiencing accelerated erosion in response to above average warming in the Arctic resulting in local, regional, and global consequences. However, Arctic coasts are expansive in scale, constituting 30\u201334% of Earth\u2019s coastline, and represent a particular challenge for wide-scale, high temporal measurement and monitoring. This study addresses the potential strengths and limitations of an object-based approach to integrate with an automated workflow by assessing the accuracy of coastal classifications and subsequent feature extraction of coastal indicator features. We tested three object-based classifications; thresholding, supervised, and a deep learning model using convolutional neural networks, focusing on a Pleaides satellite scene in the Western Canadian Arctic. Multiple spatial resolutions (0.6, 1, 2.5, 5, 10, and 30 m\/pixel) and segmentation scales (100, 200, 300, 400, 500, 600, 700, and 800) were tested to understand the wider applicability across imaging platforms. We achieved classification accuracies greater than 85% for the higher image resolution scenarios using all classification methods. Coastal features, waterline and tundra, or vegetation, line, generated from image classifications were found to be within the image uncertainty 60% of the time when compared to reference features. Further, for very high resolution scenarios, segmentation scale did not affect classification accuracy; however, a smaller segmentation scale (i.e., smaller image objects) led to improved feature extraction. Similar results were generated across classification approaches with a slight improvement observed when using deep learning CNN, which we also suggest has wider applicability. Overall, our study provides a promising contribution towards broad scale monitoring of Arctic coastal erosion.<\/jats:p>","DOI":"10.3390\/rs14132982","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T23:11:19Z","timestamp":1655939479000},"page":"2982","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Multiscale Object-Based Classification and Feature Extraction along Arctic Coasts"],"prefix":"10.3390","volume":"14","author":[{"given":"Andrew","family":"Clark","sequence":"first","affiliation":[{"name":"Department of Geography, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7565-5309","authenticated-orcid":false,"given":"Brian","family":"Moorman","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dustin","family":"Whalen","sequence":"additional","affiliation":[{"name":"Natural Resources Canada, Geological Survey of Canada\u2014Atlantic, Dartmouth, NS B3B 1A6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7611-3464","authenticated-orcid":false,"given":"Gon\u00e7alo","family":"Vieira","sequence":"additional","affiliation":[{"name":"Centre of Geographical Studies and Associated Laboratory Terra, Institute of Geography and Spatial Planning, University of Lisbon, Rua Branca Edm\u00e9e Marques, 1600-276 Lisboa, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,22]]},"reference":[{"key":"ref_1","unstructured":"Jones, B.M., Irrgang, A.M., Farquharson, L.M., Lantuit, H., Whalen, D., Ogorodov, S., Grigoriev, M., Tweedie, C., Gibbs, A.E., and Strzelecki, M.C. 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