[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290648-105":59,"doc-detail-290648-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","using-machine-learning-to-automate-image-annotation-in-rocky-intertidal-monitoring","Using Machine Learning to Automate Image Annotation in Rocky Intertidal Monitoring","","Machine-learning-based image annotation automates benthic organism labeling for rocky intertidal monitoring where manual analysis cannot keep pace with large image volumes. Field photos were collected at Bechers Bay and Skunk Point on Santa Rosa Island between 2017 and 2023 using 11 transect lines per site. CoralNet was applied to generate automated annotations aligned with CATAMI categories, then validated by comparing manual and automated outputs using regression against %cover and multiple confidence-threshold sweeps. Results summarize label call statistics, label confusion patterns, and zone-specific regression performance, concluding that automated trends can match or reveal novel patterns for key taxa.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/using-machine-learning-to-automate-image-annotation-in-rocky-intertidal-monitoring/290648/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/using-machine-learning-to-automate-image-annotation-in-rocky-intertidal-monitoring/290648.png","ImageObject",300,407,{"name":92,"@type":93},"Eden","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":8},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is machine learning used for image annotation in rocky intertidal monitoring?","Question",{"text":112,"@type":113},"Manual analysis is insufficient because the dataset is too large. Machine learning is used to automate annotations from field images and improve efficiency.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data was used to validate the CoralNet annotations?",{"text":117,"@type":113},"Photos were gathered from Bechers Bay and Skunk Point on Santa Rosa Island between 2017 and 2023, using 11 transect lines at each site.",{"name":119,"@type":110,"acceptedAnswer":120},"How were automated annotations validated against manual annotations?",{"text":121,"@type":113},"Twelve manually annotated images served as a reference set, and %cover was compared across manual and automated images via regression analysis, supported by confidence-threshold sweeps and label call summaries.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290648,1789642266,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":81},1374391974468,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","# Introduction\n\n● Manual analysis is insufficient due to extensive data.  \n● Solution:Capture field images,use machine-learning forannotations.  \n● Approach:Utilize CoralNet,a marine research tool forcollaborative image annotations.  \nMethodology  \n● Photos gathered from Bechers Bay and Skunk Point on SantaRosa Island's northeastern coast between 2017-2023.  \n● 11 transect lines at each site.  \n● Image analysis with CoralNet,a machine-learning tool forannotating benthic organism images.  \n● Annotations based off of CATAMI categories (Althaus et al.,2015).  \n□Manull Annotated(20 photos)  \n□Automatically Anotated (64 photos)  \nUsing Machine Learning to Automate ImageAnnotation in Rocky Intertidal Monitoring  \nRiley Frisk,Sophia Scipione,&Geoff Dilly  \n# Data Validation\n\n| Averager²Value   |  |  |\n| --- | --- | --- |\n| Group   | Low Zone    | Mid Zone   |\n| Algae   | 0.443  \u003Cbr>0.384   |  |\n| Mussels   | 0.898  \u003Cbr>0.327   |  |\n| Other Animals   | 0.710  \u003Cbr>0.559   |  |\n| Phyllospadix   | 0.827  \u003Cbr>0.386   |  |\n| Abiotic   | 0.664  \u003Cbr>0.392   |  |\n\n%CoverageConfidence threshold sweep  \nLabel Confusion RateCall Confidence50Threshold \\#of CallsAC APS CR EF MC>0%96000PP PX TAPE UAL>80%74678APE BR EF MM SC>85%66365SN UAI>90%10711Confidence threshold(%)5-15%BC PC SL  \n708090  \nResults  \nFigure 1.Twelve manually annotated images are chosen as areference set,and %cover is compared to a number of manualand automated images via regression analysis.  \n\n| Call Summary   | Bechers Bay   |  |  | Skunk Point   |  |  |\n| --- | --- | --- | --- | --- | --- | --- |\n|  | Total Calls    | 85%conf.call   |  conf.coverage   |  Total Calls    | 85%conf.call    | conf.coverage   |\n| Anthopluera   | 4059   | 3544   | 10.22%   | 1866   | 1495   | 3.03%   |\n| Barnacles   | 2060   | 1654   | 4.77%   | 1809   | 1324   | 2.69%   |\n| Corallines   | 279   | 184   | 0.53%   | 1865   | 1562   | 3.17%   |\n| Green Algae   | 880   | 734   | 2.12%   | 3691   | 3277   | 6.65%   |\n| Mytilus   | 1025   | 920   | 2.65%   | 5971   | 5624   | 11.41%   |\n| Phragmatopoma   | 300   | 192   | 0.55%   | 249   | 158   | 0.32%   |\n| Phyllospadix   | 2756   | 2643   | 7.62%   | 338   | 291   | 0.59%   |\n| Red Algae   | 2941   | 2369   | 6.83%   | 3241   | 2389   | 4.85%   |\n| Silvetia   | 3915   | 3765   | 10.86%   | 1080   | 971   | 1.97%   |\n| Abiotic   | 21345   | 18675   | 53.85%   | 35348   | 32212   | 65.33%   |\n| Total   | 39560   | 34680   | 100.00%   | 55458   | 49303   | 100.00%   |\n\nFigure 2.The average r²of each regression analysis by zone.Figure 3.How often each label is confused with another.  \nFigure 4.The amount of calls under different confidencethresholds.  \nFigure 5.Statistically significant changes over time forBarnacles,Red Algae,Mussels,and Phragmatopoma californica.  \nConclusions  \n| 5.   |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- |\n| Zone   |  | SsHgro   | Skunk Point Low   | Skunk Point Mid   | Bechers Bay Overal   |\n| Slope   | 0.017   | 0.028   | -0.030   | 0.013   | -0.033   |\n| p-value   | 3.96E-09   | 1.19-07   | 6.23E-08   | 1.83E-07   | 1.53E-04   |\n\n● Validation is difficult with a heterogeneous study site  \n● Barnacles and Phragmatopoma californica exhibited identicaltrends in our vertical transect findings  \nl  \n● Mussels and all red algae exhibited novel trends in automatedimage analysis findings  \nAcknowledgements  \nCited  \nLiterature  \nSpecial thanks to Scott Miller,Siomara Zendejas,Colby Klaiman,AlyssaMak,Chase Anderson,Nils Boberg,past TideFool Members,SRIRS Staff,and Island Packers.","cbCailbYPImvU6ER","https://ap.wps.com/l/cbCailbYPImvU6ER","pdf",2545778,"English","# Introduction\n# Methodology\n# Data Validation\n# Results\n# Conclusions\n# Acknowledgements","[{\"question\":\"Why is machine learning used for image annotation in rocky intertidal monitoring?\",\"answer\":\"Manual analysis is insufficient because the dataset is too large. Machine learning is used to automate annotations from field images and improve efficiency.\"},{\"question\":\"What data was used to validate the CoralNet annotations?\",\"answer\":\"Photos were gathered from Bechers Bay and Skunk Point on Santa Rosa Island between 2017 and 2023, using 11 transect lines at each site.\"},{\"question\":\"How were automated annotations validated against manual annotations?\",\"answer\":\"Twelve manually annotated images served as a reference set, and %cover was compared across manual and automated images via regression analysis, supported by confidence-threshold sweeps and label call summaries.\"}]","Using Machine Learning to Automate Image Annotation in Rocky Intertidal Monitoring | PDF"]