ISO 19178-1 Geographic information — Training Data Markup Language for Artificial Intelligence

ISO 19178-1
Working Group
Reference	Status/Date	Name	WG
ISO 19178-1	Under development	Geographic information — Training data markup language for artificial intelligence — Part 1: Conceptual model	4
ISO 19178-1	2025-05-27	Geographic information — Training Data Markup Language for Artificial Intelligence — Part 1: Conceptual Model Standard	4

Scope

Within the context of training data for Earth Observation (EO) Artificial Intelligence Machine Learning (AI/ ML), this document specifies a conceptual model that:

— establishes a UML model with a target of maximizing the interoperability and usability of EO imagery training data;
— specifies different AI/ML tasks and labels in EO in terms of supervised learning, including scene level, object level and pixel level tasks;
— describes the permanent identifier, version, licence, training data size, measurement or imagery used for annotation;
— specifies a description of quality (e.g. training data errors, training data representativeness, quality measures) and provenance (e.g. agents who perform the labelling, labelling procedure).

Document Normative References:

ISO 19101-1, Geographic information — Reference model — Part 1: Fundamentals

ISO 19103, Geographic information — Conceptual schema language

ISO 19115-1, Geographic information — Metadata — Part 1: Fundamentals

ISO 19156, Geographic information — Observations, measurements and samples

ISO 19157-1, Geographic information — Data quality — Part 1: General requirement

Referenced from active ISO/TC 211 standard:

Model References:

User Stories

Supporting the following Sustainable Development Goals:

	(Indirect support) ISO 19178‑1 supports reliable digital and data infrastructure by enabling consistent documentation and exchange of AI training data used with geospatial information. This facilitates innovation and the deployment of interoperable, trustworthy AI‑enabled systems across industry, research, and public services
	(Indirect support) By enabling transparent and reusable AI training data for geospatial and Earth‑observation applications, ISO 19178‑1 supports AI‑based analysis used in urban planning, risk assessment, and the management of complex urban environments, contributing to safer and more sustainable cities
	(Indirect support) ISO 19178‑1 facilitates consistent description and quality information for AI training data used in environmental and climate‑related applications. This supports the development of reliable AI models for climate monitoring, analysis, and decision‑making based on geospatial data.