{"id":8621,"date":"2023-09-26T09:00:00","date_gmt":"2023-09-26T13:00:00","guid":{"rendered":"https:\/\/www.ogc.org\/?p=8621"},"modified":"2023-09-26T09:00:00","modified_gmt":"2023-09-26T13:00:00","slug":"ogc-adopts-training-data-markup-language-for-artificial-intelligence-conceptual-model-as-official-standard","status":"publish","type":"post","link":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/announcement\/ogc-adopts-training-data-markup-language-for-artificial-intelligence-conceptual-model-as-official-standard\/","title":{"rendered":"OGC Adopts Training Data Markup Language for Artificial Intelligence Conceptual Model as Official Standard"},"content":{"rendered":"\n<p>The Open Geospatial Consortium (OGC) is excited to announce that the OGC Membership has approved the <a href=\"https:\/\/docs.ogc.org\/is\/23-008r3\/23-008r3.html\" rel=\"nofollow noopener\" target=\"_blank\">OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model<\/a> for adoption as an official OGC Standard. The Standard defines the conceptual model for standardized geospatial training data for Machine Learning.<\/p>\n\n\n\n<p>Training data plays a fundamental role in Earth Observation (EO) Artificial Intelligence Machine Learning (AI\/ML) applications, especially Deep Learning (DL). It is used to train, validate, and test AI\/ML models. Understanding the source and applicability of training data allows for better understanding of the results of AI\/ML operations.<\/p>\n\n\n\n<p>To maximize the interoperability and re-usability of geospatial training data, the TrainingDML-AI Standard defines a model and encodings consistent with the OGC Standards baseline to exchange and retrieve the training data via the Web. Part 1 of the Standard contains the Conceptual Model, as well as example JSON encodings. Future Parts of the Standard will cover other encodings.<\/p>\n\n\n\n<p>Additionally, the Standard provides detailed metadata for formalizing the information model of training data. This includes but is not limited to the following aspects:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>How the training data is prepared, such as provenance and quality;<\/li>\n\n\n\n<li>How to specify different metadata used for different ML tasks;<\/li>\n\n\n\n<li>How to differentiate the high-level training data information model and extended information models specific to various ML applications;<\/li>\n\n\n\n<li>How to describe the version, license, and training data size;<\/li>\n\n\n\n<li>How to introduce external classification schemes and flexible means for representing ground-truth labeling.<\/li>\n<\/ul>\n\n\n\n<p>OGC Members interested in staying up to date on future progress of this standard, or contributing to its development, are encouraged to <a href=\"https:\/\/portal.ogc.org\/index.php?m=public&amp;orderby=default&amp;tab=7\" rel=\"nofollow noopener\" target=\"_blank\">join the Training Data Markup Language for AI Standards Working Group via the OGC Portal<\/a>. Non-OGC members who would like to know more about participating in this SWG are encouraged to <a href=\"https:\/\/www.ogc.org\/contact\/\" rel=\"nofollow noopener\" target=\"_blank\">contact the OGC Standards Program<\/a>.<\/p>\n\n\n\n<p><em>As with any OGC standard, the open <\/em><a href=\"https:\/\/docs.ogc.org\/is\/23-008r3\/23-008r3.html\" rel=\"nofollow noopener\" target=\"_blank\"><em>OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard<\/em><\/a><em> is free to download and implement.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>OGC TrainingDML-AI Standard Part 1 defines the Conceptual Model for standardizing any training data used to train, validate, and test Machine Learning models that involve location or time.<\/p>\n","protected":false},"author":1,"featured_media":8622,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","footnotes":""},"categories":[169],"tags":[193,194,195,329,89],"class_list":["post-8621","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-announcement","tag-ai","tag-ai-ml","tag-earth-observation","tag-ogc-standards","tag-standards"],"acf":[],"_links":{"self":[{"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/posts\/8621","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/comments?post=8621"}],"version-history":[{"count":0,"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/posts\/8621\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/media\/8622"}],"wp:attachment":[{"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/media?parent=8621"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/categories?post=8621"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/tags?post=8621"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}