{"id":24576,"date":"2025-11-14T08:02:00","date_gmt":"2025-11-14T08:02:00","guid":{"rendered":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/?post_type=requests&p=24576"},"modified":"2025-11-25T04:07:57","modified_gmt":"2025-11-25T04:07:57","slug":"ogc-seeks-public-comment-on-proposed-deep-learning-model-standards-working-group","status":"publish","type":"requests","link":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/requests\/ogc-seeks-public-comment-on-proposed-deep-learning-model-standards-working-group\/","title":{"rendered":"OGC Seeks Public Comment on Proposed Deep Learning Model Standards Working Group"},"content":{"rendered":"\n

November 14, 2025<\/p>\n\n\n\n

The Open Geospatial Consortium (OGC) seeks public comment on the proposal to establish a new OGC Deep Learning Model Standards Working Group (SWG).<\/p>\n\n\n\n

The proposed SWG aims to develop a UML model and encoding schema to standardize the description of Deep Learning model metadata, such as framework (e.g., PyTorch, TensorFlow), neural network, training method, training data, task, quality, and license, so that models can be shared and managed in a unified, interoperable manner.<\/p>\n\n\n\n

Artificial Intelligence (AI) is expected to play a crucial role in transforming many domains and revolutionizing existing technologies. Over the past decade, Deep Learning techniques have advanced significantly due to the availability of large datasets and improvements in high-performance computing. Deep Learning is transforming Geographic Information Systems (GIS) and Remote Sensing (RS), driving innovation and efficiency in diverse areas such as smart cities, environmental management, and disaster response, while helping the scientific community harness the growing volume of Earth Observation (EO) data for geospatial analysis.<\/p>\n\n\n\n

Deep Learning models are the outcome of AI development and the core of AI application. However, the lack of a standardized way to describe Deep Learning model metadata has become a bottleneck to GeoAI advancement and application. To increase adoption of Deep Learning techniques for geospatial analysis by researchers and practitioners, several challenges must be addressed.<\/p>\n\n\n\n

    \n
  1. Lack of interoperability<\/strong>: The absence of a uniform and machine-readable description hinders model sharing and automated reuse.

    <\/li>\n\n\n\n
  2. Lack of reusability: <\/strong>Deep learning models often fail to transfer effectively across different environments due to varying dependencies and operation methods.

    <\/li>\n\n\n\n
  3. Trustworthiness concerns: <\/strong>Without meta-information, users cannot fully understand model performance or limitations, undermining credibility and transparency.

    <\/li>\n\n\n\n
  4. Lack of reproducibility: <\/strong>Variations in datasets, architectures, and hyperparameters make it difficult to compare and reproduce results.<\/li>\n<\/ol>\n\n\n\n

    To address these issues, the proposed SWG will develop a standardized metadata model for geospatial Deep Learning models that aligns with the FAIR principles – Findable, Accessible, Interoperable, and Reusable.<\/p>\n\n\n\n

    Given the popularity of JSON, the SWG <\/a>will adopt a JSON encoding consistent with common Web practices and existing industry standards.<\/p>\n\n\n\n

    To Comment<\/strong><\/h5>\n\n\n\n

    The proposed Charter for the OGC Deep Learning Model Standards Working Group (SWG)<\/a> is available for review and comment by the public and OGC members for a period of 30 days. Comments are due by December 14, 2025.<\/p>\n\n\n\n

    Comments can be submitted to a dedicated email reflector for a period ending on the \u201cClose request date\u201d listed above. Comments received will be consolidated and reviewed by OGC members for incorporation into the document. Please submit your comments via this email address<\/a>, using this Comments Template<\/a> for the message body.<\/p>\n\n\n\n

    About OGC<\/strong><\/h4>\n\n\n\n

    The Open Geospatial Consortium (OGC) is a membership organization dedicated to using the power of geography and technology to solve problems faced by people and the planet. OGC unlocks value and opportunity for its members through Standards, Innovation, and Collaboration. Our membership<\/a> represents a diverse and active global community drawn from government, industry, academia, international development agencies, research & scientific organizations, civil society, and advocates.<\/p>\n\n\n\n

    Visit ogc.org <\/a>for more information about our work.<\/p>\n","protected":false},"excerpt":{"rendered":"

    Proposed SWG to develop a metadata structure to describe Deep Learning models. <\/p>\n","protected":false},"featured_media":24577,"parent":0,"template":"","meta":{"_acf_changed":false,"_eb_attr":""},"requests-category":[146],"request-tag":[895,143,135,896,897],"class_list":["post-24576","requests","type-requests","status-publish","has-post-thumbnail","hentry","requests-category-request-for-comment-rfc","request-tag-geospatial-api","request-tag-json","request-tag-ogc-standards","request-tag-standards-working-group","request-tag-swg"],"acf":[],"_links":{"self":[{"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/requests\/24576","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/requests"}],"about":[{"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/types\/requests"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/media\/24577"}],"wp:attachment":[{"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/media?parent=24576"}],"wp:term":[{"taxonomy":"requests-category","embeddable":true,"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/requests-category?post=24576"},{"taxonomy":"request-tag","embeddable":true,"href":"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-json\/wp\/v2\/request-tag?post=24576"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}