EN ISO 19178-1:2025
(Main)Geographic information - Training data markup language for artificial intelligence - Part 1: Conceptual model (ISO 19178-1:2025)
Geographic information - Training data markup language for artificial intelligence - Part 1: Conceptual model (ISO 19178-1:2025)
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).
Information géographique - Langage de balisage des données d'entraînement pour l'intelligence artificielle - Partie 1: Modèle conceptuel (ISO 19178-1:2025)
Dans le contexte des données d’entraînement pour l’apprentissage automatique de l’intelligence artificielle (IA/ML) en matière d’observation de la Terre (EO), le présent document spécifie un modèle conceptuel qui:
— établit un modèle UML dans le but de maximiser l’interopérabilité et l’utilisabilité des données d’entraînement à l’imagerie d’observation de la Terre;
— spécifie les différentes tâches et étiquettes d’IA/ML dans le domaine de l’EO en termes d’apprentissage supervisé, y compris les tâches au niveau de la scène, de l’objet et du pixel;
— décrit l’identifiant permanent, la version, la licence, la taille des données d’entraînement, les mesures ou l’imagerie utilisée pour l’annotation;
— spécifie une description de la qualité (par exemple, les erreurs dans les données d’entraînement, la représentativité des données d’entraînement, les mesures de la qualité) et de la provenance (par exemple, les agents qui effectuent l’étiquetage, la procédure d’étiquetage).
Geografske informacije - Jezik za označevanje podatkov za usposabljanje za umetno inteligenco - 1. del: Konceptualni model (ISO/FDIS 19178-1:2025)
General Information
Standards Content (Sample)
SLOVENSKI STANDARD
oSIST prEN ISO 19178-1:2024
01-september-2024
Geografske informacije - Jezik za označevanje podatkov za usposabljanje za
umetno inteligenco - 1. del: Standard konceptualnega modela (ISO/DIS 19178-
1:2024)
Geographic information - Training data markup language for artificial intelligence - Part 1:
Conceptual model standard (ISO/DIS 19178-1:2024)
Ta slovenski standard je istoveten z: prEN ISO 19178-1
ICS:
07.040 Astronomija. Geodezija. Astronomy. Geodesy.
Geografija Geography
35.060 Jeziki, ki se uporabljajo v Languages used in
informacijski tehniki in information technology
tehnologiji
35.240.70 Uporabniške rešitve IT v IT applications in science
znanosti
oSIST prEN ISO 19178-1:2024 en,fr,de
2003-01.Slovenski inštitut za standardizacijo. Razmnoževanje celote ali delov tega standarda ni dovoljeno.
oSIST prEN ISO 19178-1:2024
oSIST prEN ISO 19178-1:2024
DRAFT
International
Standard
ISO/DIS 19178-1
ISO/TC 211
Geographic information — Training
Secretariat: SIS
data markup language for artificial
Voting begins on:
intelligence —
2024-07-10
Part 1:
Voting terminates on:
2024-10-02
Conceptual model standard
ICS: ISO ics
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Reference number
ISO/DIS 19178-1:2024(en)
oSIST prEN ISO 19178-1:2024
DRAFT
ISO/DIS 19178-1:2024(en)
International
Standard
ISO/DIS 19178-1
ISO/TC 211
Geographic information — Training
Secretariat: SIS
data markup language for artificial
Voting begins on:
intelligence —
Part 1:
Voting terminates on:
Conceptual model standard
ICS: ISO ics
THIS DOCUMENT IS A DRAFT CIRCULATED
FOR COMMENTS AND APPROVAL. IT
IS THEREFORE SUBJECT TO CHANGE
AND MAY NOT BE REFERRED TO AS AN
INTERNATIONAL STANDARD UNTIL
PUBLISHED AS SUCH.
This document is circulated as received from the committee secretariat.
IN ADDITION TO THEIR EVALUATION AS
BEING ACCEPTABLE FOR INDUSTRIAL,
© ISO 2024
TECHNOLOGICAL, COMMERCIAL AND
USER PURPOSES, DRAFT INTERNATIONAL
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
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Published in Switzerland Reference number
ISO/DIS 19178-1:2024(en)
ii
oSIST prEN ISO 19178-1:2024
ISO/DIS 19178-1:2024(en)
Contents Page
Foreword .v
Introduction .vi
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
3.1 Terms and definitions .2
3.2 Abbreviated terms .4
4 Conventions . 4
4.1 Identifiers .4
4.2 UML Notation .4
5 Conformance . 5
6 Overview . 6
6.1 AI Tasks for EO .6
6.2 Modularization .7
6.3 General Modeling Principles .7
6.3.1 Element Modeling .7
6.3.2 Class Hierarchy and Inheritance of Properties and Relations .8
6.3.3 Definition of the Semantics for all Classes, Properties, and Relations .8
6.3.4 Data Integrity, Authenticity, and Non-repudiation .8
6.4 Extending TrainingDML-AI .8
7 TrainingDML-AI UML Model . 8
7.1 ISO Dependencies .9
7.2 Overview of the UML Model .10
7.3 AI_TrainingDataset .11
7.3.1 Provisions . 12
7.3.2 Class Definitions . 13
7.4 AI_TrainningData . 13
7.4.1 Provisions .14
7.4.2 Class Definitions . 15
7.5 AI_Task . 15
7.5.1 Provisions .16
7.5.2 Class Definitions .17
7.6 AI_Label .17
7.6.1 Provisions .17
7.6.2 Class Definitions .18
7.7 AI_Labeling .18
7.7.1 Provisions .19
7.7.2 Class Definitions . 20
7.8 AI_TDChangeset . 20
7.8.1 Provisions . 20
7.8.2 Class Definitions .21
7.9 AI_DataQuality .21
7.9.1 Provisions . 22
7.9.2 Class Definitions . 23
8 TrainingDML-AI Data Dictionary .23
8.1 ISO Classes . 23
8.1.1 Feature (from ISO 19101-1:2014) . 23
8.1.2 MD_Band (from ISO 19115-1:2014) . 23
8.1.3 MD_Scope (from ISO 19115-1:2014) . . 23
8.1.4 EX_Extent (from ISO 19115-1:2014) .24
8.1.5 CI_Citation (from ISO 19115-1:2014) .24
8.1.6 DataQuality (from ISO 19157-1).24
iii
oSIST prEN ISO 19178-1:2024
ISO/DIS 19178-1:2024(en)
8.1.7 QualityElement (from ISO 19157-1) .
...
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