OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 3: XML Encoding Standard

Open Geospatial Consortium

Submission Date: 2024-06-15

Approval Date: 2024-08-27

Publication Date: 2025-02-10

External identifier of this OGC® document: http://www.opengis.net/doc/is/tdml-part3/1.0

Internal reference number of this OGC® document: 24-007r1

Version: 1.0

Editor: Peng Yue, Ruixiang Liu, Kaixuan Wang

OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 3: XML Encoding Standard

Copyright notice

To obtain additional rights of use, visit https://www.ogc.org/ogc/Document.

Warning

This document is an OGC Member approved international standard. This document is available on a royalty free, non-discriminatory basis.

Recipients of this document are invited to submit, with their comments, notification of any relevant patent rights of which they are aware and to provide supporting documentation.

Document type: OGC® Standard

Document subtype:

Document stage: Approved

Document language: English

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Table of Contents

1. Scope
2. Conformance
3. Normative References
4. Terms and Definitions
5. Conventions
- 5.1. Identifiers
- 5.2. Abbreviated Terms
6. Overview
- 6.1. Extensible Markup Language
7. Requirements for TrainingDML-AI XML Encoding
Annex A: Abstract Test Suite (Normative)
Annex B: Example (Informative)
Annex C: Revision History (Informative)
Annex D: Bibliography

i. Abstract

The OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 3: XML Encoding Standard defines requirements for encoding AI training datasets as Extensible Markup Language (XML). XML is a widely utilized format for encoding data in various applications. It employs a markup structure where data is enclosed in tags represented by opening and closing elements. These elements can have attributes and may contain nested elements, allowing for a hierarchical representation of structured information. TrainingDML-AI Part 3 is based on the OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard.

ii. Keywords

The following are keywords to be used by search engines and document catalogues.

ogcdoc, OGC document, artificial intelligence, machine learning, deep learning, earth observation, remote sensing, training data, training sample, encoding, XML

iii. Preface

Attention is drawn to the possibility that some of the elements of this document may be the subject of patent rights. The Open Geospatial Consortium shall not be held responsible for identifying any or all such patent rights.

iv. Security Considerations

No security considerations have been made for this Standard.

v. Submitting organizations

The following organizations submitted this Document to the Open Geospatial Consortium (OGC):

Wuhan University
Luoyao Information Technology Co., Ltd
Pixalytics Ltd
WiSC Enterprises
George Mason University
Laboratoire d’Informatique de Grenoble
Wuhan University of Technology
Hubei University
Chongqing Changan Zhitu Technology Co., Ltd
Hubei Luojia Laboratory

vi. Submitters

All questions regarding this submission should be directed to the editors or the submitters:

Name

Affiliation

Peng Yue

Wuhan University

Ruixiang Liu

Wuhan University

Kaixuan Wang

Wuhan University

Haoru Wu

Wuhan University

Boyi Shangguan

Luoyao Information Technology Co., Ltd

Samantha Lavender

Pixalytics Ltd

Jim Antonisse

WiSC Enterprises

Liping Di

George Mason University

Eugene Yu

George Mason University

Danielle Ziébelin

Laboratoire d’Informatique de Grenoble

Liangcun Jiang

Wuhan University of Technology

Lei Hu

Hubei University

Mingda Zhang

Hubei University

Kai Yan

Chongqing Changan Zhitu Technology Co., Ltd

vii. Acknowledgments

Thanks to the members of the TrainingDML-AI Standards Working Group of the OGC as well as all contributors of change requests and comments. In particular: Scott Simmons, Carl Reed, Sam Meek, Yongyi Mi, Chenxiao Zhang, Zhipeng Cao, Shuaiqi Liu, Ming Zhao, Hanwen Xu, Haipeng Deng, Baoxin Teng.

1. Scope

This OGC TrainingDML - AI Part 3: XML Encoding Standard defines an XML encoding for the exchange of training datasets. The TrainingDML - AI Part 3 Standard provides an XML-based encoding for the exchange of information describing training datasets, both within and between different organizations.

The document model is derived from the conceptual models defined in the OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard.

2. Conformance

This Standard defines an XML encoding for AI training datasets. The standardization target for this Standard is:

TrainingDML-AI XML Encoding Schema

Conformance with this Standard shall be checked using all the relevant tests specified in Annex A (normative) of this document. The framework, concepts, and methodology for testing, and the criteria to be achieved to claim conformance are specified in the OGC Compliance Testing Policies and Procedures and the OGC Compliance Testing website.

All requirements-classes and conformance-classes described in this document are owned by the standard identified.

This standard identifies eleven (11) conformance classes. Each conformance class is defined by one requirements class. The tests in Annex A are organized by requirements class. So an implementation of each conformance class must pass all tests specified in Annex A for the respective requirements class.

Of these eleven conformance classes, only the AI_TrainingDataset conformance class is mandatory. All other conformance classes are optional. In the case where a conformance class has a dependency on another conformance class, that conformance class should also be implemented.

If AI_TrainingDataset conformance class is implemented, all other conformance classes will eventually be implemented based on the dependencies between the conformance classes. Therefore, all conformance classes can also be considered mandatory.

3. Normative References

The following normative documents contain provisions that, through reference in this text, constitute provisions of this document. For dated references, subsequent amendments to, or revisions of, any of these publications do not apply. For undated references, the latest edition of the normative document referred to applies.

4. Terms and Definitions

This document used the terms defined in OGC Policy Directive 49, which is based on the ISO/IEC Directives, Part 2, Rules for the structure and drafting of International Standards. In particular, the word “shall” (not “must”) is the verb form used to indicate a requirement to be strictly followed to conform to this Standard and OGC documents do not use the equivalent phrases in the ISO/IEC Directives, Part 2.

For the purposes of this document, the following additional terms and definitions apply.

4.1. Artificial Intelligence (AI)

refers to a set of methods and technologies that can empower machines or software to learn and perform tasks like humans.

SOURCE: OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard

4.2. Machine Learning (ML)

is an important branch of artificial intelligence that gives computers the ability to improve their performance without explicitly being programmed to do so. ML processes create models from training data by using a set of learning algorithms, and then can use these models to make predictions. Depending on whether the training data include labels, the learning algorithms can be divided into supervised and unsupervised learning.

SOURCE: OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard

4.3. Deep Learning (DL)

is a subset of machine learning, which is essentially a neural network with three or more layers. The number of layers is referred to as depth. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy.

SOURCE: https://www.ibm.com/topics/deep-learning

4.4. Dataset

identifiable collection of data

Note 1 to entry: A dataset can be a smaller grouping of data which, though limited by some constraint such as spatial extent or feature type, is located physically within a larger dataset. Theoretically, a dataset can be as small as a single feature or feature attribute contained within a larger dataset. A hardcopy map or chart can be considered a dataset.

[SOURCE: ISO 19115‑1:2014, 4.3]

4.5. Training Dataset

is a collection of samples, often labeled in terms of supervised learning. A training dataset can be divided into training, validation, and test sets. Training samples are different from samples in OGC Observations & Measurements (O&M). They are often collected in purposive ways that deviate from purely probability sampling, with known or expected results labeled as values of a dependent variable for generating a trained predictive model.

SOURCE: OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard

4.6. Label

refers to known or expected results annotated as values of a dependent variable in training samples. A training sample label is different from those on a geographical map, which are known as map labels or annotations.

SOURCE: OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard

4.7. Class

<classification> result of a classification process as part of a classification system which subdivides concepts within a given topic area.

[SOURCE: ISO 19144-2:2023, 3.1.6]

4.8. Task

the specific goal that an AI application want to achieve.

SOURCE: OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard

4.9. Provenance

information about entities, activities, and people involved in producing a piece of data or thing, which can be used to form assessments about its quality, reliability or trustworthiness. In this standard provenance is a record of how training data were prepared.

SOURCE: W3C (https://www.w3.org/TR/prov-overview/)

4.10. Quality

degree to which a set of inherent characteristics of an object fulfils requirements [ISO 9000:2015, 3.6.2, modified－Notes 1 and 2 to entry have been deleted]. Quality of training data (such as data imbalance and mislabeling) can impact the performance of AI/ML models.

SOURCE: OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard

4.11. Earth Observation

data and information collected about our planet, whether atmospheric, oceanic or terrestrial. This includes space-based or remotely-sensed data, as well as ground-based or in situ data.

SOURCE: GEO (https://earthobservations.org/geo_wwd.php)

4.12. Scene Classification

task of identifying scene categories of images, on the basis of a training set of images whose scene categories are known.

SOURCE: OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard

4.13. Object Detection

task of recognizing objects such as cars from images. The objects are often localized using bounding boxes.

SOURCE: OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard

4.14. Semantic Segmentation

task of assigning class labels to pixels of images or points of point clouds.

SOURCE: OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard

4.15. Change Detection

task that finds the changes in an area between images taken at different times.

SOURCE: OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard

4.16. 3D Model Reconstruction

task that builds 3D objects and scenes from multi-view images.

SOURCE: OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard

4.17. Generative Model

is one of the methods of large model training, which improve model performance through unsupervised pre-training. In the fine-tuning phase, labeled data plays a critical role in optimizing the model for specific vertical domains or tasks. By incorporating labeled data, the model can learn to accurately identify and extract relevant features, leading to better performance on specific downstream tasks. Overall, the combination of generative models and fine-tuning with labeled data can significantly improve the performance of large models in specialized domains or tasks.

SOURCE: OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard

4.18. Extensible Markup Language (XML)

is a simple, very flexible text format derived from SGML (ISO 8879). Originally designed to meet the challenges of large-scale electronic publishing, XML is also playing an increasingly important role in the exchange of a wide variety of data on the Web and elsewhere.

SOURCE: https://www.w3.org/XML/

4.19. XML Schema

is a means for defining the structure, content and semantics of XML documents.

SOURCE: https://www.w3.org/XML/Schema

4.20. Training Dataset Publisher

refers to the entity or individual responsible for creating and releasing the XML-based serialization syntax for geospatial training datasets, as defined in the TrainingDML-AI Part 3: XML Encoding Standard.

5. Conventions

This section provides details and examples for any conventions used in the document.

5.1. Identifiers

The normative provisions in this Standard are denoted by the URI:

http://www.opengis.net/spec/TrainingDML-AI-3/1.0

All requirements and conformance tests that appear in this document are denoted by partial URIs which are relative to this base.

5.2. Abbreviated Terms

In this document the following abbreviations and acronyms are used or introduced:

AI — Artificial Intelligence
DL — Deep Learning
EO — Earth Observation
IETF — Internet Engineering Task Force
ISO — International Organization for Standardization
JSON — JavaScript Object Notation
ML — Machine Learning
OGC — Open Geospatial Consortium
RS — Remote Sensing
TD — Training Data
UML — Unified Modelling Language
URL — Uniform Resource Locator
UTC — Coordinated Universal Time
W3C — World Wide Web Consortium
XML — Extensible Markup Language

6. Overview

The TrainingDML - AI Part 3: XML Encoding Standard defines an XML-based serialization syntax for geospatial training datasets. While other serialization formats are possible, such alternatives are not discussed in this Standard.

XML employs a formal class model with elements and attributes, allowing for a more structured representation of data. XML documents are hierarchical in nature, composed of nested elements that can contain both data and metadata.

A training dataset document conforming to this Standard is an XML document whose root element is an "AI_TrainingDataset" element.

6.1. Extensible Markup Language

Extensible Markup Language (XML) is a versatile and widely used markup language designed for encoding documents in a format that is both human-readable and machine-readable. It provides a set of rules for defining custom markup languages and is often employed for representing structured data in a hierarchical and platform-independent manner. XML documents consist of elements, attributes, and text content organized within a tree-like structure. Elements are enclosed within tags, and attributes provide additional information about the elements.

XML has widespread applicability and is utilized in various domains, including web development, data interchange, configuration files, and more. Its flexibility allows users to define their own document structures, making it suitable for a broad range of applications. XML is supported by many programming languages and technologies, contributing to its interoperability and adoption.

When serialized, absent properties in XML are typically represented by leaving the corresponding elements empty, without any content. Alternatively, certain XML-based formats may use attributes to signify the absence of a property. These representations convey semantic equivalence. An empty or missing element signifies that no value has been assigned, distinct from the interpretation that the provided value is empty or nil.

7. Requirements for TrainingDML-AI XML Encoding

7.1. Requirements Class: Base

7.1.1. Requirements Class: XML Base Type

The XML Base Type requirements class defines the base requirements for XML encodings, which includes definitions of common types used in the TrainingDML-AI XML encoding.

Requirements class
/req/base/xmlbasetype
Dependency	XML
Requirement 1	/req/base/xmlbasetype/xml
Requirement 2	/req/base/xmlbasetype/datetime
Requirement 3	/req/base/xmlbasetype/namedvalue
Requirement 4	/req/base/xmlbasetype/url

Requirements class

/req/base/xmlbasetype

Dependency

XML

Requirement 1

/req/base/xmlbasetype/xml

Requirement 2

/req/base/xmlbasetype/datetime

Requirement 3

/req/base/xmlbasetype/namedvalue

Requirement 4

/req/base/xmlbasetype/url

The first requirement is that a TrainingDML-AI XML document is a valid XML document.

Requirement 1

/req/base/xmlbasetype/xml

An instance SHALL be a conformant XML document, as defined by W3C Recommendation

XML has a limited range of built-in types (https://www.w3.org/TR/xmlschema11-2/). The following requirements provide standard XML representations of additional types required across all requirements within this specification.

The DateTime is encoded as an XML element defined as one of three elements of type "date", "time", "dateTime".

Requirement 2

/req/base/xmlbasetype/datetime

Each DateTime value SHALL be encoded as a text string defined in Section 3.3.7, Section 3.3.8 or Section 3.3.9 of W3C XML Schema Definition Language (XSD) 1.1 Part 2: Datatypes.

The specification of date and time in any XML encoding of training set data SHALL be specified in UTC.

Examples:

a)   2002-05-30T09:00:00
b)   2002-05-30T09:30:10.5
c)   2002-05-30T09:30:10Z
d)   2002-09-24
e)   2002-09-24Z
f)   09:30:10
g)   09:30:10.5
h)   09:30:10Z

The NamedValue is encoded as an XML element with two elements named “key” and “value”. Typically, the "key" element represents a unique identifier or name for the value being described, while the "value" element contains the actual data associated with that identifier. The "key" element is an open field allowing for arbitrary keys to be used. The "value" element may be any types.

Requirement 3

/req/base/xmlbasetype/namedvalue

Each NamedValue value SHALL be encoded as an XML element with elements "key" and "value", while the type of "key" is CharacterString.

Examples:

a) <key>forest</key>
   <value>RGB(0,255,255)</value>
b) <key>precision</key>
   <value>0.8</value>

The URL is encoded as an XML element defined as the type of “anyURI”.

Requirement 4

/req/base/xmlbasetype/url

Each URL value SHALL be encoded as a text string defined in Section 3.3.17 of W3C XML Schema Definition Language (XSD) 1.1 Part 2: Datatypes.

Examples:

a) http://www.opengeospatial.org
b) /file.txt

7.1.2. Requirements Class: ISO Metadata Type

The ISO Metadata Type requirements class defines the requirements for XML encoding of ISO metadata types.

Requirements class
/req/base/isometadatatype
Dependency	XML
Requirement 5	/req/base/isometadatatype/band
Requirement 6	/req/base/isometadatatype/extent
Requirement 7	/req/base/isometadatatype/citation
Requirement 8	/req/base/isometadatatype/scope

Requirements class

/req/base/isometadatatype

Dependency

XML

Requirement 5

/req/base/isometadatatype/band

Requirement 6

/req/base/isometadatatype/extent

Requirement 7

/req/base/isometadatatype/citation

Requirement 8

/req/base/isometadatatype/scope

The MD_Band is encoded as an XML element.

Requirement 5

/req/base/isometadatatype/band

Each MD_Band value SHALL be encoded as an XML element matching the XML Schema type as defined in:

https://schemas.isotc211.org/19115/-1/mrc/1.3.0/content.xsd

Examples:

a) <mrc:name>
       <mcc:MD_Identifier>
           <mcc:code>
               <gco:CharacterString>red</gco:CharacterString>
           </mcc:code>
       </mcc:MD_Identifier>
   </mrc:name>
b) <mrc:name>
       <mcc:MD_Identifier>
           <mcc:code>
               <gco:CharacterString>B4</gco:CharacterString>
           </mcc:code>
       </mcc:MD_Identifier>
   </mrc:name>
c) <mrc:boundMax>
       <gco:Real>690</gco:Real>
   </mrc:boundMax>
   <mrc:boundMin>
       <gco:Real>630</gco:Real>
   </mrc:boundMin>
   <mrc:boundUnits>
       <gml:UnitDefinition gml:id="ID">
           <gml:identifier codeSpace="http://www.example.com/">nm</gml:identifier>
       </gml:UnitDefinition>
   </mrc:boundUnits>

An The EX_Extent is encoded as an XML element.

Requirement 6

/req/base/isometadatatype/extent

Each EX_Extent value SHALL be encoded as an XML element matching the XML Schema type as defined in:

https://schemas.isotc211.org/19115/-1/gex/1.3.0/extent.xsd

Examples:

a) <gex:geographicElement>
       <gex:EX_BoundingPolygon>
           <gex:polygon>
               <gml:Polygon>
                   <gml:exterior>
                       <gml:LinearRing>
                           <gml:posList>
                               51.556272 -0.2803943 51.5562758 -0.2787397 51.5556539 -0.278736 51.5556501 -0.2803906 51.556272 -0.2803943
                           </gml:posList>
                       </gml:LinearRing>
                   </gml:exterior>
               </gml:Polygon>
           </gex:polygon>
       </gex:EX_BoundingPolygon>
   </gex:geographicElement>
b) <gex:geographicElement>
       <gex:EX_GeographicBoundingBox>
           <gex:westBoundLongitude>
               <gco:Decimal>-171.76409</gco:Decimal>
           </gex:westBoundLongitude>
           <gex:eastBoundLongitude>
               <gco:Decimal>-157.86768</gco:Decimal>
           </gex:eastBoundLongitude>
           <gex:southBoundLatitude>
               <gco:Decimal>-14.42443</gco:Decimal>
           </gex:southBoundLatitude>
           <gex:northBoundLatitude>
               <gco:Decimal>21.31573</gco:Decimal>
           </gex:northBoundLatitude>
       </gex:EX_GeographicBoundingBox>
   </gex:geographicElement>

The CI_Citation is encoded as an XML element.

Requirement 7

/req/base/isometadatatype/citation

Each CI_Citation value SHALL be encoded as an XML element matching the XML Schema type as defined in:

https://schemas.isotc211.org/19115/-1/cit/1.3.0/citation.xsd

Example:

<cit:title>
    <gco:CharacterString>Open Geospatial Consortium</gco:CharacterString>
</cit:title>
<cit:alternateTitle>
    <gco:CharacterString>OGC</gco:CharacterString>
</cit:alternateTitle>
<cit:identifier>
    <mcc:MD_Identifier>
        <mcc:code>
            <gco:CharacterString>artifact_id=104605</gco:CharacterString>
        </mcc:code>
        <mcc:version>
            <gco:CharacterString>1</gco:CharacterString>
        </mcc:version>
    </mcc:MD_Identifier>
</cit:identifier>

The MD_Scope is encoded as an XML element.

Requirement 8

/req/base/isometadatatype/scope

Each MD_Scope value SHALL be encoded as an XML element matching the XML Schema type as defined in:

https://schemas.isotc211.org/19115/-1/mcc/1.3.0/commonClasses.xsd

Examples:

a) <mcc:level>
       <mcc:MD_ScopeCode codeList="http://standards.iso.org/iso/19115/resources/Codelist/cat/codelists.xml#MD_ScopeCode" codeListValue="dataset"/>
   </mcc:level>
   <mcc:levelDescription>
       <mcc:MD_ScopeDescription>
           <mcc:dataset>
               <gco:CharacterString>whu_rs19</gco:CharacterString>
           </mcc:dataset>
       </mcc:MD_ScopeDescription>
   </mcc:levelDescription>
b) <mcc:level>
       <mcc:MD_ScopeCode codeList="http://standards.iso.org/iso/19115/resources/Codelist/cat/codelists.xml#MD_ScopeCode" codeListValue="attribute"/>
   </mcc:level>
   <mcc:levelDescription>
       <mcc:MD_ScopeDescription>
           <mcc:attributes>
               <gco:CharacterString>completeness</gco:CharacterString>
           </mcc:attributes>
       </mcc:MD_ScopeDescription>
   </mcc:levelDescription>
c) <mcc:level>
       <mcc:MD_ScopeCode codeList="http://standards.iso.org/iso/19115/resources/Codelist/cat/codelists.xml#MD_ScopeCode" codeListValue="coverage"/>
   </mcc:level>
   <mcc:extent>
       <gex:EX_Extent>
           <gex:geographicElement>
               <gex:EX_GeographicBoundingBox>
                   <gex:westBoundLongitude>
                       <gco:Decimal>-171.76409</gco:Decimal>
                   </gex:westBoundLongitude>
                   <gex:eastBoundLongitude>
                       <gco:Decimal>-157.86768</gco:Decimal>
                   </gex:eastBoundLongitude>
                   <gex:southBoundLatitude>
                       <gco:Decimal>-14.42443</gco:Decimal>
                   </gex:southBoundLatitude>
                   <gex:northBoundLatitude>
                       <gco:Decimal>21.31573</gco:Decimal>
                   </gex:northBoundLatitude>
               </gex:EX_GeographicBoundingBox>
           </gex:geographicElement>
       </gex:EX_Extent>
   </mcc:extent>
d) <mcc:level>
       <mcc:MD_ScopeCode codeList="http://standards.iso.org/iso/19115/resources/Codelist/cat/codelists.xml#MD_ScopeCode" codeListValue="feature"/>
   </mcc:level>
   <mcc:extent>
       <gex:EX_Extent>
           <gex:geographicElement>
               <gex:EX_BoundingPolygon>
                   <gex:polygon>
                       <gml:Polygon>
                           <gml:exterior>
                               <gml:LinearRing>
                                   <gml:posList>
                                       51.556272 -0.2803943 51.5562758 -0.2787397 51.5556539 -0.278736 51.5556501 -0.2803906 51.556272 -0.2803943
                                   </gml:posList>
                               </gml:LinearRing>
                           </gml:exterior>
                       </gml:Polygon>
                   </gex:polygon>
               </gex:EX_BoundingPolygon>
           </gex:geographicElement>
       </gex:EX_Extent>
   </mcc:extent>

7.1.3. Requirements Class: ISO Quality Type

The ISO Quality Type requirements class defines the requirements for XML encoding of ISO quality types.

Requirements class
/req/base/isoqualitytype
Dependency	XML
Requirement 9	/req/base/isoqualitytype/element

Requirements class

/req/base/isoqualitytype

Dependency

XML

Requirement 9

/req/base/isoqualitytype/element

The QualityElement object is encoded as an XML element with properties shown in Table 1.

Requirement 9

/req/base/isoqualitytype/element

Each QualityElement value SHALL be encoded as an XML element with properties shown in Table 1.

Table 1. QualityElement properties
XML Property	Definition	Data type and values	Obligation
type	The type of the quality element object.	CharacterString [1..1]	Mandatory
measure	Reference to measure used.	MeasureReference [1..1]	Mandatory
evaluationMethod	Evaluation information.	EvaluationMethod [1..1]	Mandatory
result	Value obtained from applying a data quality measure.	QualityResult [1..*]	Mandatory

Example:

<type>FormatConsistency</type>
<measure>
   <measureDescription>
      Percentage of training samples with inconsistent image format
   </measureDescription>
</measure>
<evaluationMethod>
   <evaluationMethodDescription>
      Full test method to calculate the percentage of training samples with an inconsistent format
   </evaluationMethodDescription>
</evaluationMethod>
<result>
   <quantitativeResult>
      <value>
         0
      </value>
      <valueUnit>
         %
      </valueUnit>
   </quantitativeResult>
</result>

7.1.4. Requirements Class: Geospatial Type

The Geospatial Type requirements class defines the requirements for XML encoding of geospatial types.

Requirements class
/req/base/geospatialtype
Dependency	XML
Dependency	GML
Requirement 10	/req/base/geospatialtype/feature

Requirements class

/req/base/geospatialtype

Dependency

XML

Dependency

GML

Requirement 10

/req/base/geospatialtype/feature

The encoding of one or more features follows GML for encoding a Feature element, with members “type”, “geometry” and “properties”. A Feature element represents a spatially bounded thing. Every Feature element is a GML element no matter where it occurs in a GML text.

Requirement 10

/req/base/geospatialtype/feature

Each Feature value SHALL be encoded using the GML feature encoding defined by OpenGIS in GML Section 3.2:

http://www.opengis.net/gml/3.2

Examples of Feature encodings are:

a) <gml:metaDataProperty>
       <gml:GenericMetaData>
           <truncated>0.0</truncated>
           <occluded>0</occluded>
           <alpha>-0.2</alpha>
       </gml:GenericMetaData>
   </gml:metaDataProperty>
   <gml:location>
       <gml:GeometricComplex>
           <gml:element>
               <gml:Polygon>
                   <gml:exterior>
                       <gml:LinearRing>
                           <gml:posList>
                               51.556272 -0.2803943 51.5562758 -0.2787397 51.5556539 -0.278736 51.5556501 -0.2803906 51.556272 -0.2803943
                           </gml:posList>
                       </gml:LinearRing>
                   </gml:exterior>
               </gml:Polygon>
           </gml:element>
       </gml:GeometricComplex>
   </gml:location>
b) <gml:metaDataProperty>
       <gml:GenericMetaData>
           <iscrowd>0</iscrowd>
           <area>2580</area>
       </gml:GenericMetaData>
   </gml:metaDataProperty>
   <gml:location>
       <gml:GeometricComplex>
           <gml:element>
               <gml:LineString>
                   <gml:posList>1.0 1.0 2.0 2.0</gml:posList>
               </gml:LineString>
           </gml:element>
       </gml:GeometricComplex>
   </gml:location>
c) <gml:location>
       <gml:GeometricComplex>
           <gml:element>
               <gml:Point>
                   <gml:pos>1.0 1.0</gml:pos>
               </gml:Point>
           </gml:element>
       </gml:GeometricComplex>
   </gml:location>

7.2. Requirements Class: AI_TrainingDataset

The AI_TrainingDataset requirements class defines an XML encoding for the AI_TrainingDataset module, which is based on the UML model specified in the TrainingDML-AI Part 1: Conceptual Model Standard.

Requirements class
/req/aitrainingdataset
Dependency	XML
Dependency	/req/base/xmlbasetype
Dependency	/req/base/isometadatatype
Dependency	/req/aitrainingdata
Dependency	/req/aitask
Dependency	/req/ailabeling
Dependency	/req/aidataquality
Dependency	/req/aitdchangeset
Requirement 11	/req/aitrainingdataset/trainingdataset
Requirement 12	/req/aitrainingdataset/metricsinliterature
Requirement 13	/req/aitrainingdataset/eotrainingdataset

Requirements class

/req/aitrainingdataset

Dependency

XML

Dependency

/req/base/xmlbasetype

Dependency

/req/base/isometadatatype

Dependency

/req/aitrainingdata

Dependency

/req/aitask

Dependency

/req/ailabeling

Dependency

/req/aidataquality

Dependency

/req/aitdchangeset

Requirement 11

/req/aitrainingdataset/trainingdataset

Requirement 12

/req/aitrainingdataset/metricsinliterature

Requirement 13

/req/aitrainingdataset/eotrainingdataset

The AI_TrainingDataset object is encoded as an XML element with properties shown in Table 2.

Requirement 11

/req/aitrainingdataset/trainingdataset

Each AI_TrainingDataset object SHALL implement the Mandatory properties shown in Table 2.

Table 2. AI_TrainingDataset properties
XML Property	Definition	Data type and values	Obligation
type	Type of the training dataset.	"AI_AbstractTrainingDataset"	Mandatory
id	Identification of the AI training dataset.	CharacterString [1..1]	Mandatory
doi	Digital object identifier of the AI training dataset.	CharacterString [0..1]	Optional
scope	Description of the scope of the training dataset.	MD_Scope [0..1]	Optional
name	Name of the AI training dataset.	CharacterString [1..1]	Mandatory
description	Description of the AI training dataset.	CharacterString [1..1]	Mandatory
version	Version number of the AI training dataset.	CharacterString [0..1]	Optional
amountOfTrainingData	Total number of training samples in the AI training dataset.	Int [0..1]	Optional
createdTime	Time when the AI training dataset was created.	DateTime [0..1]	Optional
updatedTime	Time when the AI training dataset was updated.	DateTime [0..1]	Optional
license	License description of the AI training dataset.	CharacterString [1..1]	Mandatory
providers	People or organizations who provide the AI training dataset.	CharacterString [0..*]	Optional
keywords	Keywords of the AI training dataset.	CharacterString [0..*]	Optional
metricsInLIT	Results of performance metrics achieved by AI/ML algorithms in the peer-reviewed literature.	AI_MetricsInLiterature [0..*]	Optional
statisticsInfo	Statistical results for training samples in each class.	NamedValue [0..*]	Optional
dataSources	Citation for the data sources.	CI_Citation [0..*]	Optional
numberOfClasses	Total number of classes in the AI training dataset.	Int [0..1]	Optional
classificationSchema	Classification schema for classes used in the AI training dataset.	CharacterString [0..1]	Optional
classes	Classes used in the AI training dataset.	NamedValue [1..1]	Optional
tasks	Task description of the training dataset.	AI_Task [1..*]	Mandatory
labeling	Provenance information of how the training dataset is labeled.	AI_Labeling [0..*]	Optional
quality	Quality information of the training dataset.	DataQuality [0..*]	Optional
changesets	Changeset between two versions of the training dataset.	AI_TDChangeset [0..*]	Optional
data	Training data in the training dataset.	AI_AbstractTrainingData [1..*]	Mandatory

Example:

<type>AI_AbstractTrainingDataset</type>
<id>whu_rs19</id>
<name>WHU-RS19</name>
<description>Wuhan University-Remote Sensing 19 Categories (WHU-RS19) has 19 classes of remote sensing images scenes obtained from Google Earth</description>
<license>CC BY-SA 4.0</license>
<amountOfTrainingData>1013</amountOfTrainingData>
<createdTime>2010-01-01T00:00:00</createdTime>
<providers>Wuhan University</providers>
<keywords>Remote Sensing</keywords>
<keywords>Scene Classification</keywords>
<numberOfClasses>19</numberOfClasses>
<classes>
   <key>Airport</key>
   <value>null</value>
</classes>
<classes>
   <key>Beach</key>
   <value>null</value>
</classes>
<classes>
   <key>Bridge</key>
   <value>null</value>
</classes>
<classes>
   <key>Commercial</key>
   <value>null</value>
</classes>
<classes>
   <key>Desert</key>
   <value>null</value>
</classes>
<classes>
   <key>Farmland</key>
   <value>null</value>
</classes>
<classes>
   <key>footballField</key>
   <value>null</value>
</classes>
<classes>
   <key>Forest</key>
   <value>null</value>
</classes>
<classes>
   <key>Industrial</key>
   <value>null</value>
</classes>
<classes>
   <key>Meadow</key>
   <value>null</value>
</classes>
<classes>
   <key>Mountain</key>
   <value>null</value>
</classes>
<classes>
   <key>Park</key>
   <value>null</value>
</classes>
<classes>
   <key>Parking</key>
   <value>null</value>
</classes>
<classes>
   <key>Pond</key>
   <value>null</value>
</classes>
<classes>
   <key>Port</key>
   <value>null</value>
</classes>
<classes>
   <key>railwayStation</key>
   <value>null</value>
</classes>
<classes>
   <key>Residential</key>
   <value>null</value>
</classes>
<classes>
   <key>River</key>
   <value>null</value>
</classes>
<classes>
   <key>Viaduct</key>
   <value>null</value>
</classes>
<tasks>
   <type>AI_EOTask</type>
   <id>whu_rs19-task</id>
   <description>Structural high-resolution satellite image indexing</description>
   <taskType>Scene Classification</taskType>
</tasks>
<data>
   <type>AI_EOTrainingData</type>
   <id>airport_01</id>
   <dataSources>
      <cit:title>
         <gco:CharacterString>googleEarth</gco:CharacterString>
      </cit:title>
   </dataSources>
   <dataURL>image/Airport/airport_01.jpg</dataURL>
   <labels>
      <type>AI_SceneLabel</type>
      <class>Airport</class>
   </labels>
</data>

If the optional element AI_MetricsInLiterature is specified, this element is encoded as XML element with properties as shown in Table 3.

Requirement 12

/req/aitrainingdataset/metricsinliterature

Each AI_MetricsInLiterature value SHALL implement the Mandatory properties shown in Table 3.

Table 3. AI_MetricsInLiterature properties
XML Property	Definition	Data type and values	Obligation
doi	Digital object identifier of the peer-reviewed literature.	CharacterString [1..1]	Mandatory
algorithm	AI/ML algorithms used in the peer-reviewed literature.	CharacterString [0..1]	Optional
metrics	Metrics and results of AI/ML algorithms in the peer-reviewed literature.	NamedValue [1..*]	Mandatory

Example:

<doi>10.1109/TGRS.2019.2917161</doi>
<algorithm>FACNN</algorithm>
<metrics>
   <key>Overall Accuracy</key>
   <value>0.9881</value>
</metrics>

The AI_EOTrainingDataset object is encoded as an XML element with properties shown in Table 2 and Table 4.

Requirement 13

/req/aitrainingdataset/eotrainingdataset

Each AI_EOTrainingDataset object SHALL implement the Mandatory properties both shown in Table 2 and Table 4.

Table 4. AI_EOTrainingDataset properties
XML Property	Definition	Data type and values	Obligation
type	Type of the training dataset.	"AI_EOTrainingDataset"	Mandatory
extent	Spatial extent of the EO training dataset.	EX_Extent [0..1]	Optional
bands	Bands description of the images used in the EO training dataset.	MD_Band [0..*]	Optional
imageSize	Size of the images used in the EO training dataset.	ChracterString [0..1]	Optional

Example:

<type>AI_EOTrainingDataset</type>
<id>whu_rs19</id>
<name>WHU-RS19</name>
<description>Wuhan University-Remote Sensing 19 Categories (WHU-RS19) has 19 classes of remote sensing images scenes obtained from Google Earth</description>
<license>CC BY-SA 4.0</license>
<amountOfTrainingData>1013</amountOfTrainingData>
<createdTime>2010-01-01T00:00:00</createdTime>
<providers>Wuhan University</providers>
<keywords>Remote Sensing</keywords>
<keywords>Scene Classification</keywords>
<numberOfClasses>19</numberOfClasses>
<classes>
   <key>Airport</key>
   <value>null</value>
</classes>
<classes>
   <key>Beach</key>
   <value>null</value>
</classes>
<classes>
   <key>Bridge</key>
   <value>null</value>
</classes>
<classes>
   <key>Commercial</key>
   <value>null</value>
</classes>
<classes>
   <key>Desert</key>
   <value>null</value>
</classes>
<classes>
   <key>Farmland</key>
   <value>null</value>
</classes>
<classes>
   <key>footballField</key>
   <value>null</value>
</classes>
<classes>
   <key>Forest</key>
   <value>null</value>
</classes>
<classes>
   <key>Industrial</key>
   <value>null</value>
</classes>
<classes>
   <key>Meadow</key>
   <value>null</value>
</classes>
<classes>
   <key>Mountain</key>
   <value>null</value>
</classes>
<classes>
   <key>Park</key>
   <value>null</value>
</classes>
<classes>
   <key>Parking</key>
   <value>null</value>
</classes>
<classes>
   <key>Pond</key>
   <value>null</value>
</classes>
<classes>
   <key>Port</key>
   <value>null</value>
</classes>
<classes>
   <key>railwayStation</key>
   <value>null</value>
</classes>
<classes>
   <key>Residential</key>
   <value>null</value>
</classes>
<classes>
   <key>River</key>
   <value>null</value>
</classes>
<classes>
   <key>Viaduct</key>
   <value>null</value>
</classes>
<extent>
   <gex:geographicElement>
      <gex:EX_GeographicBoundingBox>
         <gex:westBoundLongitude>
            <gco:Decimal>-180</gco:Decimal>
         </gex:westBoundLongitude>
         <gex:eastBoundLongitude>
            <gco:Decimal>180</gco:Decimal>
         </gex:eastBoundLongitude>
         <gex:southBoundLatitude>
            <gco:Decimal>-90</gco:Decimal>
         </gex:southBoundLatitude>
         <gex:northBoundLatitude>
            <gco:Decimal>90</gco:Decimal>
         </gex:northBoundLatitude>
      </gex:EX_GeographicBoundingBox>
   </gex:geographicElement>
</extent>
<bands>
   <mrc:name>
      <mcc:MD_Identifier>
         <mcc:code>
            <gco:CharacterString>red</gco:CharacterString>
         </mcc:code>
      </mcc:MD_Identifier>
   </mrc:name>
</bands>
<bands>
   <mrc:name>
      <mcc:MD_Identifier>
         <mcc:code>
            <gco:CharacterString>green</gco:CharacterString>
         </mcc:code>
      </mcc:MD_Identifier>
   </mrc:name>
</bands>
<bands>
   <mrc:name>
      <mcc:MD_Identifier>
         <mcc:code>
            <gco:CharacterString>blue</gco:CharacterString>
         </mcc:code>
      </mcc:MD_Identifier>
   </mrc:name>
</bands>
<imageSize>6000x7600</imageSize>
<tasks>
   <type>AI_EOTask</type>
   <id>whu_rs19-task</id>
   <description>Structural high-resolution satellite image indexing</description>
   <taskType>Scene Classification</taskType>
</tasks>
<data>
   <type>AI_EOTrainingData</type>
   <id>airport_01</id>
   <dataSources>
      <cit:title>
         <gco:CharacterString>googleEarth</gco:CharacterString>
      </cit:title>
   </dataSources>
   <dataURL>image/Airport/airport_01.jpg</dataURL>
   <labels>
      <type>AI_SceneLabel</type>
      <class>Airport</class>
   </labels>
</data>

7.3. Requirements Class: AI_TrainingData

The AI_TrainingData requirements class defines an XML encoding for the AI_TrainingData module, which is based on the UML model specified in the TrainingDML-AI Part 1: Conceptual Model Standard.

Requirements class
/req/aitrainingdata
Dependency	XML
Dependency	/req/base/xmlbasetype
Dependency	/req/base/isometadatatype
Dependency	/req/ailabel
Dependency	/req/ailabeling
Dependency	/req/aidataquality
Requirement 14	/req/aitrainingdata/trainingdata
Requirement 15	/req/aitrainingdataset/trainingtypecode
Requirement 16	/req/aitrainingdata/eotrainingdata

Requirements class

/req/aitrainingdata

Dependency

XML

Dependency

/req/base/xmlbasetype

Dependency

/req/base/isometadatatype

Dependency

/req/ailabel

Dependency

/req/ailabeling

Dependency

/req/aidataquality

Requirement 14

/req/aitrainingdata/trainingdata

Requirement 15

/req/aitrainingdataset/trainingtypecode

Requirement 16

/req/aitrainingdata/eotrainingdata

The AI_TrainingData object is encoded as an XML element with properties shown in Table 5.

Requirement 14

/req/aitrainingdataset/trainingdata

Each AI_TrainingData object SHALL implement the Mandatory properties shown in Table 5.

Table 5. AI_TrainingData properties
XML Property	Definition	Data type and values	Obligation
type	Type of the training sample.	"AI_AbstractTrainingData"	Mandatory
id	Identification of an individual AI training sample.	CharacterString [1..1]	Mandatory
datasetId	Identification of the training dataset that the training sample belongs to.	CharacterString [0..1]	Optional
trainingType	Training type of the individual AI training sample.	AI_TrainingTypeCode [0..1]	Optional
numberOfLabels	Total number of labels in the individual AI training sample.	Int [0..1]	Optional
dataSources	Citation of inputs to prepare a training sample.	CI_Citation [0..*]	Optional
labels	Labels in the training data.	AI_Label [1..*]	Mandatory
labeling	Provenance information of how the training data is labeled.	AI_Labeling [0..*]	Optional
quality	Quality information of the training data.	DataQuality [0..*]	Optional

Example:

<type>AI_AbstractTrainingData</type>
<id>airport_01</id>
<dataSources>
   <cit:title>
      <gco:CharacterString>googleEarth</gco:CharacterString>
   </cit:title>
</dataSources>
<dataURL>image/Airport/airport_01.jpg</dataURL>
<labels>
   <type>AI_SceneLabel</type>
   <class>Airport</class>
</labels>

The AI_TrainingTypeCode is encoded as a text string whose value is one of “training”, “validation”, “test” or “retraining”.

Requirement 15

/req/aitrainingdataset/trainingtypecode

Each AI_TrainingTypeCode value SHALL be a text string whose value is one of “training”, “validation”, “test” or “retraining”.

Examples:

a) training
b) validation
c) test
d) retraining

The AI_EOTrainingData object is encoded as an XML element with properties both shown in Table and Table 6.

Requirement 16

/req/aitrainingdataset/eotrainingdata

Each AI_EOTrainingData object SHALL implement the Mandatory properties as defined in Table 5 and Table 6.

Table 6. AI_EOTrainingData properties
XML Property	Definition	Data type and values	Obligation
type	Type of the EO training data.	"AI_EOTrainingData"	Mandatory
extent	Spatial extent of the individual EO training sample.	EX_Extent [0..1]	Optional
dateTime	Date time when the EO data was obtained.	DateTime [0..*]	Optional
dataURL	URL of the EO data, including both relative and absolute paths, which can encompass local paths, network addresses, and more.	URL [1..*]	Mandatory

Example:

<type>AI_EOTrainingData</type>
<id>airport_01</id>
<dataSources>
   <cit:title>
      <gco:CharacterString>googleEarth</gco:CharacterString>
   </cit:title>
</dataSources>
<extent>
   <gex:geographicElement>
      <gex:EX_GeographicBoundingBox>
         <gex:westBoundLongitude>
            <gco:Decimal>-171.76409</gco:Decimal>
         </gex:westBoundLongitude>
         <gex:eastBoundLongitude>
            <gco:Decimal>-171.56578</gco:Decimal>
         </gex:eastBoundLongitude>
         <gex:southBoundLatitude>
            <gco:Decimal>-14.42443</gco:Decimal>
         </gex:southBoundLatitude>
         <gex:northBoundLatitude>
            <gco:Decimal>-14.32568</gco:Decimal>
         </gex:northBoundLatitude>
      </gex:EX_GeographicBoundingBox>
   </gex:geographicElement>
</extent>
<dataTime>2002-05-30T09:30:10Z</dataTime>
<dataURL>image/Airport/airport_01.jpg</dataURL>
<labels>
   <type>AI_SceneLabel</type>
   <class>Airport</class>
</labels>

7.4. Requirements Class: AI_Task

The AI_Task requirements class defines an XML encoding for the AI_Task module, which is based on the UML model specified in the TrainingDML-AI Part 1: Conceptual Model Standard.

Requirements class
/req/aitask
Dependency	XML
Dependency	/req/base/xmlbasetype
Requirement 17	/req/aitask/task
Requirement 18	/req/aitask/eotask

Requirements class

/req/aitask

Dependency

XML

Dependency

/req/base/xmlbasetype

Requirement 17

/req/aitask/task

Requirement 18

/req/aitask/eotask

The AI_Task object is encoded as an XML element with properties as shown in Table 7.

Requirement 17

/req/aitask/task

Each AI_Task object SHALL implement the Mandatory properties shown in Table 7.

Table 7. AI_Task properties
XML Property	Definition	Data type and values	Obligation
type	Type of the task object.	"AI_AbstractTask"	Mandatory
id	Identification of the task.	CharacterString [1..1]	Mandatory
datasetId	Identification of the training dataset the training sample belongs to.	CharacterString [0..1]	Optional
description	Description of the AI task.	CharacterString [0..1]	Optional

Example:

<type>AI_AbstractTask</type>
<id>image-indexing-task</id>
<description>Structural high-resolution satellite image indexing</description>

The AI_EOTask object is encoded as an XML element with properties both shown in Table 7 and Table 8.

Requirement 18

/req/aitask/eotask

Each AI_EOTask object SHALL implement the Mandatory properties shown in Table 7 and Table 8.

Table 8. AI_EOTask properties
XML Property	Definition	Data type and values	Obligation
type	Type of the task object.	"AI_EOTask"	Mandatory
taskType	Type of the EO task.	CharacterString [1..1]	Mandatory

Example:

<type>AI_EOTask</type>
<id>image-indexing-task</id>
<description>Structural high-resolution satellite image indexing</description>
<taskType>Scene Classification</taskType>

7.5. Requirements Class: AI_Label

The AI_Label requirements class defines an XML encoding for the AI_Label module, which is based on the UML model specified in the TrainingDML-AI Part 1: Conceptual Model Standard.

Requirements class
/req/ailabel
Dependency	XML
Dependency	/req/base/xmlbasetype
Dependency	/req/base/geospatialtype
Requirement 19	/req/ailabel/label
Requirement 20	/req/ailabel/scenelabel
Requirement 21	/req/ailabel/objectlabel
Requirement 22	/req/ailabel/pixellabel
Requirement 23	/req/ailabel/imageformatcode

Requirements class

/req/ailabel

Dependency

XML

Dependency

/req/base/xmlbasetype

Dependency

/req/base/geospatialtype

Requirement 19

/req/ailabel/label

Requirement 20

/req/ailabel/scenelabel

Requirement 21

/req/ailabel/objectlabel

Requirement 22

/req/ailabel/pixellabel

Requirement 23

/req/ailabel/imageformatcode

The AI_Label object is encoded as an XML element with properties as shown in Table 9.

Requirement 19

/req/ailabel/label

Each AI_Label object SHALL implement the Mandatory properties shown in Table 9.

Table 9. AI_Label properties
XML Property	Definition	Data type and values	Obligation
type	Type of the label object.	"AI_AbstractLabel"	Mandatory
isNegative	Whether the training sample related to the label is a positive or negative sample.	Bool [0..1] Default: false	Optional
confidence	Confidence score of the labeler.	Float [0..1] Default: 1.0 Range: [0, 1]	Optional

Example:

<type>AI_AbstractLabel</type>
<isNegative>false</isNegative>
<confidence>1.0</confidence>

The AI_SceneLabel object is encoded as an XML element with properties as shown in Table 10.

Requirement 20

/req/ailabel/scenelabel

Each AI_SceneLabel object SHALL implement the Mandatory properties shown in Table 10.

Table 10. AI_SceneLabel properties
XML Property	Definition	Data type and values	Obligation
type	Type of the label object at the scene level.	"AI_SceneLabel"	Mandatory
class	Class that records the semantic of the scene of the training sample.	CharacterString [1..1]	Mandatory

Example:

<type>AI_SceneLabel</type>
<class>Airport</class>

The AI_ObjectLabel object is encoded as an XML element with properties shown in Table 11.

Requirement 21

/req/ailabel/objectlabel

Each AI_ObjectLabel object SHALL implement the Mandatory properties shown in Table 11.

Table 11. AI_ObjectLabel properties
XML Property	Definition	Data type and values	Obligation
type	Type of the label object at the object level.	"AI_ObjectLabel"	Mandatory
object	Feature that represents the position and attributes of the object.	Feature [0..1]	Mandatory
bboxType	Type of the bbox.	CharacterString [0..1]	Optional
class	Class that records the semantic of the object type.	CharacterString [1..1]	Mandatory
dateTime	Created time of the object label.	DateTime [0..1]	Optional

Example:

<type>AI_ObjectLabel</type>
<object>
   <gml:metaDataProperty>
      <gml:GenericMetaData>
         <truncated>0.0</truncated>
         <occluded>0</occluded>
         <alpha>-0.2</alpha>
      </gml:GenericMetaData>
   </gml:metaDataProperty>
   <gml:location>
      <gml:GeometricComplex>
         <gml:element>
            <gml:Polygon>
               <gml:exterior>
                  <gml:LinearRing>
                     <gml:posList>
                        51.556272 -0.2803943 51.5562758 -0.2787397 51.5556539 -0.278736 51.5556501 -0.2803906 51.556272 -0.2803943
                     </gml:posList>
                  </gml:LinearRing>
               </gml:exterior>
            </gml:Polygon>
         </gml:element>
      </gml:GeometricComplex>
   </gml:location>
</object>
<bboxType>Horizontal BBox</bboxType>
<class>Pedestrian</class>

The AI_PixelLabel object is encoded as an XML element with properties as shown in Table 12.

Requirement 22

/req/ailabel/pixellabel

Each AI_PixelLabel object SHALL implement the Mandatory properties shown in Table 12.

Table 12. AI_PixelLabel properties
XML Property	Definition	Data type and values	Obligation
type	Type of the label object at the pixel level.	"AI_PixelLabel"	Mandatory
imageURL	URL of the images representing the label information.	URL [1..*]	Mandatory
imageFormat	Image data format.	AI_ImageFormatCode [1..*]	Mandatory

Example:

<type>AI_PixelLabel</type>
<imageURL>/label_5classes/GF2_PMS1__L1A0000647767-MSS1_label.tif</imageURL>
<imageFormat>image/tiff; application=geotiff</imageFormat>

The AI_ImageFormatCode is encoded as a text string whose value is defined in Multipurpose Internet Mail Extensions (MIME) Part Two: Media Types RFC 2046.

Requirement 23

/req/ailabel/imageformatcode

Each AI_ImageFormatCode value SHALL be encoded as a text string defined in Multipurpose Internet Mail Extensions (MIME) Part Two: Media Types RFC 2046.

Examples:

a)   image/tiff; application=geotiff
b)   application/x-netcdf
c)   image/png
d)   image/jp2

7.6. Requirements Class: AI_Labeling

The AI_Labeling requirements class defines an XML encoding for the AI_Labeling module, which is based on the UML model specified in the TrainingDML-AI Part 1: Conceptual Model Standard.

Requirements class
/req/ailabeling
Dependency	XML
Dependency	/req/base/xmlbasetype
Dependency	/req/base/isometadatatype
Requirement 24	/req/ailabeling/labeling
Requirement 25	/req/ailabeling/labeler
Requirement 26	/req/ailabeling/labelingprocedure
Requirement 27	/req/ailabeling/labelingmethodcode

Requirements class

/req/ailabeling

Dependency

XML

Dependency

/req/base/xmlbasetype

Dependency

/req/base/isometadatatype

Requirement 24

/req/ailabeling/labeling

Requirement 25

/req/ailabeling/labeler

Requirement 26

/req/ailabeling/labelingprocedure

Requirement 27

/req/ailabeling/labelingmethodcode

The AI_Labeling object is encoded as an XML element with properties shown in Table 13.

Requirement 24

/req/ailabeling/labeling

Each AI_Labeling object SHALL implement the Mandatory properties shown in Table 13.

Table 13. AI_ Labeling properties
XML Property	Definition	Data type and values	Obligation
type	Type of the labeling object.	"AI_Labeling"	Mandatory
id	Identifier of the labeling.	CharacterString [1..1]	Mandatory
scope	Description of the scope of the labeling.	MD_Scope [1..1]	Mandatory
labelers	Labelers of the labeling activity.	AI_Labeler [0..*]	Optional
procedure	Procedure used in the labeling activity.	AI_LabelingProcedure [0..1]	Optional

Example:

<type>AI_Labeling</type>
<id>0</id>
<scope>
   <mcc:level>
     <mcc:MD_ScopeCode codeList="http://standards.iso.org/iso/19115/resources/Codelist/cat/codelists.xml#MD_ScopeCode" codeListValue="dataset"/>
   </mcc:level>
   <mcc:levelDescription>
      <mcc:MD_ScopeDescription>
         <mcc:dataset>
             <gco:CharacterString>whu_rs19</gco:CharacterString>
         </mcc:dataset>
      </mcc:MD_ScopeDescription>
   </mcc:levelDescription>
</scope>
<labelers>...</labelers>
<procedure>...</procedure>

The AI_Labeler object is encoded as an XML element with properties as shown in Table 14.

Requirement 25

/req/ailabeling/labeler

Each AI_Labeler object SHALL implement the Mandatory properties shown in Table 14.

Table 14. AI_ Labeler properties
XML Property	Definition	Data type and values	Obligation
type	Type of the labeler object.	"AI_Labeler"	Mandatory
id	Identifier of the labeler.	CharacterString [1..1]	Mandatory
name	Name of the labeler.	CharacterString [1..1]	Mandatory

Example:

<type>AI_Labeler</type>
<id>0</id>
<name>Tom</name>

The AI_LabelingProcedure object is encoded as an XML element with properties as shown in Table 15.

Requirement 26

/req/ailabeling/labelingprocedure

Each AI_LabelingProcedure object SHALL implement the Mandatory properties shown in Table 15.

Table 15. AI_ LabelingProcedure properties
XML Property	Definition	Data type and values	Obligation
type	Type of the labeling procedure object.	"AI_LabelingProcedure"	Mandatory
id	Identifier of the labeling procedure.	CharacterString [1..1]	Mandatory
methods	Methods used in the labeling procedure.	AI_LabelingMethodCode [1..*]	Mandatory
tools	Tools or software used in the labeling procedure.	CharacterString [0..*]	Optional

Example:

<type>AI_LabelingProcedure</type>
<id>0</id>
<methods>manual</methods>
<tools>ArcGIS</tools>

The AI_LabelingMethodCode is encoded as a text string whose value is one of “manual”, “semi-automatic”, “automatic” or “unkonwn”.

Requirement 27

/req/ailabeling/labelingmethodcode

Each AI_LabelingMethodCode value SHALL be a text string whose value is one of “manual”, “semi-automatic”, “automatic” or “unknown”.

Examples:

a)   manual
b)   semi-automatic
c)   automatic
d)   unknown

7.7. Requirements Class: AI_DataQuality

The AI_DataQuality requirements class defines an XML encoding for the AI_DataQuality module, which is based on the UML model specified in the TrainingDML-AI Part 1: Conceptual Model Standard.

Requirements class

/req/aidataquality

Dependency

XML

Dependency

/req/base/xmlbasetype

Dependency

/req/base/isometadatatype

Dependency

/req/base/isoqualitytype

Requirement 28

/req/aidataquality/classbalancedegree

The AI_ClassBalanceDegree object is encoded as an XML element with properties as shown in Table 16.

Requirement 28

/req/aidataquality/classbalancedegree

Each AI_ClassBalanceDegree object SHALL implement the Mandatory properties as shown in Table 16.

Table 16. AI_ ClassBalanceDegree properties
XML Property	Definition	Data type and values	Obligation
type	Type of the class balance degree object.	"AI_ClassBalanceDegree"	Mandatory
measure	Reference to measure used.	MeasureReference [1..1]	Mandatory
evaluationMethod	Evaluation information.	EvaluationMethod [1..1]	Mandatory
result	Value obtained from applying a data quality measure.	QualityResult [1..*]	Mandatory

Example:

<type>AI_ClassBalanceDegree</type>
<measure>
  <measureDescription>
    Balance degree of label classes
  </measureDescription>
</measure>
<evaluationMethod>
  <evaluationMethodDescription>
    Counting the number of training samples belonging to each class and calculating the balance degree
  </evaluationMethodDescription>
</evaluationMethod>
<result>
  <quantitativeResult>
      <value>
        93.5
      </value>
      <valueUnit>
        %
      </valueUnit>
  </quantitativeResult>
</result>

7.8. Requirements Class: AI_TDChangeset

The AI_TDChangeset requirements class defines an XML encoding for the AI_TDChangeset module, which is based on the UML model specified in the TrainingDML-AI Part 1: Conceptual Model Standard.

Requirements class

/req/aitdchangeset

Dependency

XML

Dependency

/req/base/xmlbasetype

Dependency

/req/base/isometadatatype

Dependency

/req/tdtrainingdata

Requirement 29

/req/aitdchangeset/tdchangeset

The AI_TDChangeset object is encoded as an XML element with properties shown in Table 17.

Requirement 29

/req/aitdchangeset/tdchangeset

Each AI_TDChangeset object SHALL implement the Mandatory properties as shown in Table 17.

Table 17. AI_ TDChangeset properties
XML Property	Definition	Data type and values	Obligation
type	Type of the TD changeset object.	"AI_TDChangeset"	Mandatory
id	Identifier of the changeset.	CharacterString [1..1]	Mandatory
datasetId	Identifier of the training dataset the changeset belongs to.	CharacterString [0..1]	Optional
version	Version of the training dataset that the changeset belongs to.	CharacterString [0..1]	Optional
changeCount	Total number of changed training samples.	Int [1..1]	Mandatory
createdTime	The time that the changeset was created.	DateTime [0..1]	Optional
add	Added training samples.	AI_AbstractTrainingData [0..*]	Optional
modify	Modified training samples.	AI_AbstractTrainingData [0..*]	Optional
delete	Deleted training samples.	AI_AbstractTrainingData [0..*]	Optional

Example:

<type>AI_TDChangeset</type>
<id>changeset-dota_v1.5</id>
<datasetId>dota_v1.5</datasetId>
<createdTime>2019-01-01T00:00:00</createdTime>
<changeCount>9</changeCount>
<modify>
   <type>EOTrainingData</type>
   <id>P1228</id>
   <dataSources>
      <cit:title>
         <gco:CharacterString>GF</gco:CharacterString>
      </cit:title>
   </dataSources>
   <dataURL>train/images/P1228.png</dataURL>
   <numberOfLabels>50</numberOfLabels>
   <trainingType>training</trainingType>
   <labels>
      <type>AI_ObjectLabel</type>
      <object>
         <gml:location>
            <gml:GeometricComplex>
               <gml:element>
                  <gml:Polygon>
                     <gml:exterior>
                        <gml:LinearRing>
                           <gml:posList>
                              2306.0 729.0 2330.0 729.0 2330.0 744.0 2306.0 744.0 2306.0 729.0
                           </gml:posList>
                        </gml:LinearRing>
                     </gml:exterior>
                  </gml:Polygon>
               </gml:element>
            </gml:GeometricComplex>
         </gml:location>
      </object>
      <bboxType>Horizontal BBox</bboxType>
      <class>ship</class>
   </labels>
</modify>

Annex A: Abstract Test Suite (Normative)

A.1. Introduction

Conformance is tested using the XML Schema document which formalize the requirements described above.

A.2. Conformance Class: Base

The Base conformance class tests that occurrences of the basic types are encoded according to the requirements.

A.2.1. Conformance Class: XML Base Type

Conformance Class

http://www.opengis.net/spec/TrainingDML-AI-3/1.0/conf/base/xmlbasetype

Requirements class

/req/base/xmlbasetype

Abstract Test 1

Test ID

/conf/base/xmlbasetype/xml

Requirement

/req/base/xmlbasetype/xml

Test purpose

Verify that the document is well-formed XML.

Test method

Load the document in an XML validator. Pass if no errors reported. Fail otherwise.

Abstract Test 2

Test ID

/conf/base/xmlbasetype/datetime

Requirement

/req/base/xmlbasetype/datetime

Test purpose

Verify that XML instance documents claiming conformance to this specification contain valid DateTime values according to Section 3.3.7, Section 3.3.8 or Section 3.3.9 of W3C XML Schema Definition Language (XSD) 1.1 Part 2: Datatypes.

Test method

Inspect the instance document to verify the above requirement.

Abstract Test 3

Test ID

/conf/base/xmlbasetype/namedvalue

Requirement

/req/base/xmlbasetype/namedvalue

Test purpose

Verify that XML instance documents claiming conformance to this specification validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/namedValue.xsd.

Test method

Validate the instance document against the namedValue.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Abstract Test 4

Test ID

/conf/base/xmlbasetype/url

Requirement

/req/base/xmlbasetype/url

Test purpose

Verify that XML instance documents claiming conformance to this specification contain valid URL values according to the W3C definition of anyURI, which represents an Internationalized Resource Identifier Reference. An anyURI value can be absolute or relative and may have an optional fragment identifier.

Test method

Inspect the instance document to verify the above requirement.

A.2.2. Conformance Class: ISO Metadata Type

Conformance Class

http://www.opengis.net/spec/TrainingDML-AI-3/1.0/conf/base/isometadatatype

Requirements class

/req/base/isometadatatype

Abstract Test 5

Test ID

/conf/base/isometadatatype/band

Requirement

/req/base/isometadatatype/band

Test purpose

Verify that instance documents using the MD_Band XML elements validate against the XML schema specified in https://schemas.isotc211.org/19115/-1/mrc/1.3.0/content.xsd.

Test method

Validate the instance document against the content.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Abstract Test 6

Test ID

/conf/base/isometadatatype/extent

Requirement

/req/base/isometadatatype/extent

Test purpose

Verify that instance documents using the EX_Extent XML elements validate against the XML schema specified in https://schemas.isotc211.org/19115/-1/gex/1.3.0/extent.xsd.

Test method

Validate the instance document against the extent.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Abstract Test 7

Test ID

/conf/base/isometadatatype/citation

Requirement

/req/base/isometadatatype/citation

Test purpose

Verify that instance documents using the CI_Citation XML elements validate against the XML schema specified in https://schemas.isotc211.org/19115/-1/cit/1.3.0/citation.xsd.

Test method

Validate the instance document against the citation.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Abstract Test 8

Test ID

/conf/base/isometadatatype/scope

Requirement

/req/base/isometadatatype/scope

Test purpose

Verify that instance documents using the MD_Scope XML elements validate against the XML schema specified in https://schemas.isotc211.org/19115/-1/mcc/1.3.0/commonClasses.xsd.

Test method

Validate the instance document against the commonClasses.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

A.2.3. Conformance Class: ISO Quality Type

Conformance Class

http://www.opengis.net/spec/TrainingDML-AI-3/1.0/conf/base/isoqualitytype

Requirements class

/req/base/isoqualitytype

Abstract Test 9

Test ID

/conf/base/isoqualitytype/element

Requirement

/req/base/isoqualitytype/element

Test purpose

Verify that instance documents using the QualityElement XML elements validate against the XML schema specified in https://schemas.isotc211.org/schemas/19157/-/mdq/1.2.0/dataQualityElement.xsd.

Test method

Validate the instance document against the dataQualityElement.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

A.2.4. Conformance Class: Geospatial Type

Conformance Class

http://www.opengis.net/spec/TrainingDML-AI-3/1.0/conf/base/geospatialtype

Requirements class

/req/base/geospatialtype

Abstract Test 10

Test ID

/conf/base/geospatialtype/feature

Requirement

/req/base/geospatialtype/feature

Test purpose

Verify that instance documents using the Feature XML elements validate against the XML schema specified in http://schemas.opengis.net/gml/3.2.1/gml.xsd.

Test method

Validate the instance document against the gml.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

A.3. Conformance Class: AI_TrainingDataset

The AI_TrainingDataset conformance class tests that the training dataset object is encoded according to the requirements.

Conformance Class

http://www.opengis.net/spec/TrainingDML-AI-3/1.0/conf/aitrainingdataset

Requirements class

/req/aitrainingdataset

Dependency

/conf/base/xmlbasetype

Dependency

/conf/base/isometadatatype

Dependency

/conf/aitrainingdata

Dependency

/conf/aitask

Dependency

/conf/ailabeling

Dependency

/conf/aidataquality

Dependency

/conf/aitdchangeset

Abstract Test 11

Test ID

/conf/aitrainingdataset/trainingdataset

Requirement

/req/aitrainingdataset/trainingdataset

Test purpose

Verify that instance documents using the AI_TrainingDataset XML elements listed in Table 2 validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_trainingDataset.xsd.

Test method

Validate the instance document against the ai_trainingDataset.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Abstract Test 12

Test ID

/conf/aitrainingdataset/metricsinliterature

Requirement

/req/aitrainingdataset/metricsinliterature

Test purpose

Verify that instance documents using the AI_MetricsInLiterature XML elements listed in Table 3 validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_metricsInLiterature.xsd.

Test method

Validate the instance document against the ai_metricsInLiterature.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Abstract Test 13

Test ID

/conf/aitrainingdataset/eotrainingdataset

Requirement

/req/aitrainingdataset/eotrainingdataset

Test purpose

Verify that instance documents using the AI_EOTrainingDataset XML elements listed in Table 2 and Table 4 validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_eoTrainingDataset.xsd.

Test method

Validate the instance document against the ai_eoTrainingDataset.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

A.4. Conformance Class: AI_TrainingData

The AI_TrainingData conformance class tests that the training data objects are encoded according to the requirements.

Conformance Class

http://www.opengis.net/spec/TrainingDML-AI-3/1.0/conf/aitrainingdata

Requirements class

/req/aitrainingdata

Dependency

/conf/base/xmlbasetype

Dependency

/conf/base/isometadatatype

Dependency

/conf/ailabel

Dependency

/conf/ailabeling

Dependency

/conf/aidataquality

Abstract Test 14

Test ID

/conf/aitrainingdata/trainingdata

Requirement

/req/aitrainingdata/trainingdata

Test purpose

Verify that instance documents using the AI_TrainingData XML elements listed in Table 5 validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_trainingData.xsd.

Test method

Validate the instance document against the ai_trainingData.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Abstract Test 15

Test ID

/conf/aitrainingdata/trainingtypecode

Requirement

/req/aitrainingdata/trainingtypecode

Test purpose

Verify that instance documents using the AI_TrainingTypeCode XML elements validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_trainingTypeCode.xsd.

Test method

Validate the instance document against the ai_trainingTypeCode.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Abstract Test 16

Test ID

/conf/aitrainingdata/eotrainingdata

Requirement

/req/aitrainingdata/eotrainingdata

Test purpose

Verify that instance documents using the AI_EOTrainingData XML elements listed in Table 5 and Table 6 validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_eoTrainingData.xsd.

Test method

Validate the instance document against the ai_eoTrainingData.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

A.5. Conformance Class: AI_Task

The AI_Task conformance class tests that the task objects are encoded according to the requirements.

Conformance Class

http://www.opengis.net/spec/TrainingDML-AI-3/1.0/conf/aitask

Requirements class

/req/aitask

Dependency

/conf/base/xmlbasetype

Abstract Test 17

Test ID

/conf/aitask/task

Requirement

/req/aitask/task

Test purpose

Verify that instance documents using the AI_Task XML elements listed in Table 7 validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_task.xsd.

Test method

Validate the instance document against the ai_task.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Abstract Test 18

Test ID

/conf/aitask/eotask

Requirement

/req/aitask/eotask

Test purpose

Verify that instance documents using the AI_EOTask XML elements listed in Table 7 and Table 8 validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_eoTask.xsd.

Test method

Validate the instance document against the ai_eoTask.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

A.6. Conformance Class: AI_Label

The AI_Label conformance class tests that the label objects are encoded according to the requirements.

Conformance Class

http://www.opengis.net/spec/TrainingDML-AI-3/1.0/conf/ailabel

Requirements class

/req/ailabel

Dependency

/conf/base/xmlbasetype

Dependency

/conf/base/geospatialtype

Abstract Test 19

Test ID

/conf/ailabel/label

Requirement

/req/ailabel/label

Test purpose

Verify that instance documents using the AI_Label XML elements listed in Table 9 validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_label.xsd.

Test method

Validate the instance document against the ai_label.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Abstract Test 20

Test ID

/conf/ailabel/scenelabel

Requirement

/req/ailabel/scenelabel

Test purpose

VVerify that instance documents using the AI_SceneLabel XML elements listed in Table 10 validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_sceneLabel.xsd.

Test method

Validate the instance document against the ai_sceneLabel.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Abstract Test 21

Test ID

/conf/ailabel/objectlabel

Requirement

/req/ailabel/objectlabel

Test purpose

Verify that instance documents using the AI_ObjectLabel XML elements listed in Table 11 validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_objectLabel.xsd.

Test method

Validate the instance document against the ai_objectLabel.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Abstract Test 22

Test ID

/conf/ailabel/pixellabel

Requirement

/req/ailabel/pixellabel

Test purpose

Verify that instance documents using the AI_PixelLabel XML elements listed in Table 12 validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_pixelLabel.xsd.

Test method

Validate the instance document against the ai_pixelLabel.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Abstract Test 23

Test ID

/conf/ailabel/imageformatcode

Requirement

/req/ailabel/imageformatcode

Test purpose

Verify that instance documents using the AI_ImageFormatCode XML elements conform to the requirements specified by the Multipurpose Internet Mail Extensions (MIME) Part Two: Media Types RFC 2046.

Test method

Inspect the instance document to verify the above requirement.

A.7. Conformance Class: AI_Labeling

The AI_Labeling conformance class tests that the labeling objects are encoded according to the requirements.

Conformance Class

http://www.opengis.net/spec/TrainingDML-AI-3/1.0/conf/ailabeling

Requirements class

/req/ailabeling

Dependency

/conf/base/xmlbasetype

Dependency

/conf/base/isometadatatype

Abstract Test 24

Test ID

/conf/ailabeling/labeling

Requirement

/req/ailabeling/labeling

Test purpose

Verify that instance documents using the AI_Labeling XML elements listed in Table 13 validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_labeling.xsd.

Test method

Validate the instance document against the ai_labeling.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Abstract Test 25

Test ID

/conf/ailabeling/labeler

Requirement

/req/ailabeling/labeler

Test purpose

Verify that instance documents using the AI_Labeler XML elements listed in Table 14 validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_labeler.xsd.

Test method

Validate the instance document against the ai_labeler.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Abstract Test 26

Test ID

/conf/ailabeling/labelingprocedure

Requirement

/req/ailabeling/labelingprocedure

Test purpose

Verify that instance documents using the AI_LabelingProcedure XML elements listed in Table 15 validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_labelingProcedure.xsd.

Test method

Validate the instance document against the ai_labelingProcedure.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Abstract Test 27

Test ID

/conf/ailabeling/labelingmethodcode

Requirement

/req/ailabeling/labelingmethodcode

Test purpose

Verify that instance documents using the AI_LabelingMethodCode XML elements validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_labelingMethodCode.xsd.

Test method

Validate the instance document against the ai_labelingMethodCode.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

A.8. Conformance Class: AI_DataQuality

The AI_DataQuality conformance class tests that the data quality objects are encoded according to the requirements.

Conformance Class

http://www.opengis.net/spec/TrainingDML-AI-3/1.0/conf/aidataquality

Requirements class

/req/aidataquality

Dependency

/conf/base/xmlbasetype

Dependency

/conf/base/isometadatatype

Dependency

/conf/base/isoqualitytype

Abstract Test 28

Test ID

/conf/aidataquality/classbalancedegree

Requirement

/req/aidataquality/classbalancedegree

Test purpose

Verify that instance documents using the AI_ClassBalanceDegree XML elements listed in Table 16 validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_classBalanceDegree.xsd.

Test method

Validate the instance document against the ai_classBalanceDegree.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

A.9. Conformance Class: AI_TDChangeset

The AI_TDChangeset conformance class tests that the TD changeset objects are encoded according to the requirements.

Conformance Class

http://www.opengis.net/spec/TrainingDML-AI-3/1.0/conf/aitdchangeset

Requirements class

/req/aitdchangeset

Dependency

/conf/base/xmlbasetype

Dependency

/conf/aitrainingdata

Abstract Test 29

Test ID

/conf/aitdchangeset/tdchangeset

Requirement

/req/aitdchangeset/tdchangeset

Test purpose

Verify that instance documents using the AI_TDChangeset XML elements listed in Table 17 validate against the XML schema specified in http://schemas.opengis.net/trainingdml-ai/part3/1.0/ai_tdChangeset.xsd.

Test method

Validate the instance document against the ai_tdChangeset.xsd schema to verify the above requirement. The process may be using an appropriate software tool for validation or be a manual process that checks all definitions from the XML schema specification.

Annex B: Example (Informative)

B.1. TrainingDataset Encoding Examples

B.1.1. WHU-RS19 Dataset

The WHU-RS19 dataset is widely used in scene classification of remote sensing images. This dataset is collected from Google Earth and has 19 classes including airport, beach, bridge, commercial, desert, farmland, football field, forest, industrial, meadow, mountain, park, parking, pond, port, railway station, residential, river, and viaduct. Each class contains around 50 images, with an image size of 600×600 and a resolution of 0.5 m.

An example of XML encoding of the WHU-RS19 dataset following the TrainingDML-AI UML model can be found in https://github.com/opengeospatial/TrainingDML-AI_SWG/tree/main/use-cases/examples/1.0/WHU-RS19.xml.

B.1.2. DOTA-v1.5 Dataset

The DOTA-v1.5 dataset is a large-scale dataset for object detection in aerial images. The sources for content in the dataset include Google Earth, Gaofen-2, and Jilin-1 imagery provided by China Resources Satellite Data Center. The 16 classes in DOTA-v1.5 are plane, ship, storage tank, baseball diamond, tennis court, basketball court, ground track field, harbor, bridge, large vehicle, small vehicle, helicopter, roundabout, soccer ball field, swimming pool, and container crane. Compared with other aerial image object detection datasets, the dataset has the largest number of classes. The images in the dataset have various image sizes (from 800×800 to 2000×2000) and resolutions (Google Earth/0.1 m-1 m, Gaofen-2/1 m, Jilin-1/0.72 m).

An example of XML encoding of the DOTA-v1.5 dataset following the TrainingDML-AI UML model can be found in https://github.com/opengeospatial/TrainingDML-AI_SWG/tree/main/use-cases/examples/1.0/DOTA-v1.5.xml.

B.1.3. KITTI 2D Object Detection Dataset

The KITTI 2D object detection dataset is a novel open-access dataset and benchmark for road area and ego-lane detection. KITTI 2D consists of 7481 annotated training images of high variability from the KITTI autonomous driving platform by two PointGrey Flea2 color cameras, capturing a broad spectrum of urban street views and road scenes. The eight (8) classes in the KITTI 2D object detection dataset are car, van, truck, pedestrian, person_sitting, cyclist, tram, and misc. Compared with other street view object detection datasets, this dataset compresses diverse scenarios and captures real-world traffic situations, ranging from freeways over rural areas to inner-city scenes with many static and dynamic objects.

An example of XML encoding of the KITTI 2D object detection dataset following the TrainingDML-AI UML model can be found in https://github.com/opengeospatial/TrainingDML-AI_SWG/tree/main/use-cases/examples/1.0/KITTI.xml.

B.1.4. GID Dataset

The GID dataset is one of state-of-art land cover classification datasets. This dataset has a large spatial coverage covering many provinces in China with a relatively high spatial resolution (2 m). GID has two sets. One is the GID-5C. It has 150 images (image size 7200×6800) that are classified into 5 land cover classes. The other set is GID-15C. The images from GID-5C are sliced into 30,000 patches in GID-15C, which have three types of patch sizes (56×56, 112×112, 224×224) and are classified into 15 land cover classes.

An example of XML encoding of the GID-5C dataset following the TrainingDML-AI UML model can be found in https://github.com/opengeospatial/TrainingDML-AI_SWG/tree/main/use-cases/examples/1.0/GID-5C.xml.

B.1.5. Toronto3D Dataset

The Toronto3D dataset is a large urban outdoor point cloud dataset for segmentation collected by the Mobile Laser Scanning System. The dataset covers about 1 km of scene streets in Toronto, including four areas named L001, L002, L003, and L004, with a total of 78.3 million points. Each point in this dataset has 10 attributes representing the 3D position, RGB color, intensity, GPS time, scan angle rank, and category, respectively. This dataset has eight categories, including road, road mark, natural, building, utility line, pole, car, and fence.

An example of XML encoding of the Toronto3D dataset following the TrainingDML-AI UML model can be found in https://github.com/opengeospatial/TrainingDML-AI_SWG/tree/main/use-cases/examples/1.0/Toronto_3D.xml.

B.1.6. WHU-Building Dataset

The WHU-Building dataset is a change detection dataset collected from the Land Information New Zealand Data Service. The dataset is composed of images (with the resolution 0.2 m) in 2012 and 2016, covering 20.5 km2. It includes 12,796 and 16,077 buildings respectively in 2012 and 2016.

An example of XML encoding of the WHU-Building dataset following the TrainingDML-AI UML model can be found in https://github.com/opengeospatial/TrainingDML-AI_SWG/tree/main/use-cases/examples/1.0/WHU-building.xml.

B.1.7. California Change Detection Dataset

The California Change Detection Dataset is composed of two images and a label image. The first image is a Landsat 8 acquisition covering Sacramento County, Yuba County and Sutter County, California, on 5 January 2017. It has nine channels covering the spectrum from deep blue to short-wave infrared, plus two long-wave infrared channels. The second image was acquired on 18 February 2017 by Sentinel-1A over the same area after the occurrence of a flood. The image is recorded in polarizations VV and VH and augmented with the ratio between the two intensities as a third channel. All these channels are log-transformed.

An example of XML encoding of the California change detection dataset following the TrainingDML-AI UML model can be found in https://github.com/opengeospatial/TrainingDML-AI_SWG/tree/main/use-cases/examples/1.0/UiT_HCD_California_2017.xml.

B.1.8. WHU MVS Dataset

The WHU MVS dataset is a synthetic aerial dataset created for large-scale and high-resolution Earth surface reconstruction. The basic training sample of the dataset is a multi-view unit consisting of five aerial images, and their corresponding depth maps are taken as ground truth. There are a total of 5680 pairs of five-view aerial images in the dataset. All the images are simulated from a 3D surface model, which is produced by Smart3D software using Unmanned Aerial Vehicle (UAV) images and refined by manual editing.

An example of XML encoding of the WHU MVS dataset following the TrainingDML-AI UML model can be found in https://github.com/opengeospatial/TrainingDML-AI_SWG/tree/main/use-cases/examples/1.0/WHU_MVS.xml.

B.1.9. iSAID Dataset

The iSAID dataset is the first benchmark dataset for instance segmentation in aerial images. This large-scale and densely annotated dataset contains 655,451 object instances for 15 categories across 2,806 high-resolution images. The images of iSAID is the same as the DOTA-v1.0 dataset, which are mainly collected from the Google Earth, some are taken by satellite JL-1, the others are taken by satellite GF-2 of the China Centre for Resources Satellite Data and Application. The object categories in iSAID include: plane, ship, storage tank, baseball diamond, tennis court, basketball court, ground track field, harbor, bridge, large vehicle, small vehicle, helicopter, roundabout, soccer ball field and swimming pool.

An example of XML encoding of the iSAID dataset following the TrainingDML-AI UML model can be found in https://github.com/opengeospatial/TrainingDML-AI_SWG/blob/main/use-cases/examples/1.0/iSAID.xml.

B.2. DataQuality Encoding Example

B.2.1. WHU-RS19 Data Quality

An encoded data quality example of the WHU-RS19 datasets following the TrainingDML-AI UML model can be found in https://github.com/opengeospatial/TrainingDML-AI_SWG/tree/main/use-cases/examples/1.0/WHU-RS19-quality.xml.

B.3. TDChangeset Encoding Example

B.3.1. DOTA-v1.5 Changeset

DOTA-v1.5 uses the same images as DOTA-v1.0, but the extremely small instances (less than 10 pixels) are also annotated. Moreover, a new category "container crane" is added. It contains 403,318 instances in total. The number of images and dataset splits are the same as DOTA-v1.0. This version was released for the DOAI Challenge 2019 on Object Detection in Aerial Images in conjunction with IEEE CVPR 2019.

An encoded changeset example between the DOTA-v1.0 and DOTA-v1.5 datasets following the TrainingDML-AI UML model can be found in https://github.com/opengeospatial/TrainingDML-AI_SWG/tree/main/use-cases/examples/1.0/DOTA-v1.5-changeset.xml.

B.4. Non-EO Imagery TrainingDataset Encoding Examples

B.4.1. ERA5 Dataset

The ERA5 dataset is derived from in-situ observational data (Copernicus product), and we limit its usage scenario to the autoregression problem of time series data. Therefore, its label is the data itself. Similar to unsupervised learning, the autoregression task for time series data does not require additional labeled data. For this dataset, inheritance classes for AI_AbstractLabel are not defined, although this class is required in the existing standard (please note that these test cases are for future versions of the standard). In addition, additional attributes to support the complete representation of dataset information were added.

An example of XML encoding of the ERA5 dataset following the TrainingDML-AI UML model can be found in https://github.com/opengeospatial/TrainingDML-AI_SWG/blob/main/use-cases/examples/1.0/ERA5_hourly_data.xml.

B.4.2. SCIERC Dataset

The SCIERC dataset is derived from textual data, and its labels are the classification of the text. This dataset is a text classification problem, with the goal of information extraction and entity recognition. For this textual dataset, the Abstract class is inherited and AI_TextTrainingDataset, AI_TextTrainingData, AI_TextTask, and AI_EntityLabel respectively are defined. In addition, additional attributes to support the complete representation of dataset information were added.

An example of XML encoding of the SCIERC dataset following the TrainingDML-AI UML model can be found in https://github.com/opengeospatial/TrainingDML-AI_SWG/blob/main/use-cases/examples/1.0/SCIRec.xml.

B.4.3. nuScenes Dataset

The nuScenes dataset is a public large-scale dataset for autonomous driving developed by the team at Motional (formerly nuTonomy). The full dataset includes approximately 1.4M camera images, 390k LIDAR sweeps, 1.4M RADAR sweeps and 1.4M object bounding boxes in 40k keyframes. Although the training data may come from different domains, the 3D annotation boxes captured by numerous sensors in the same keyframe are targeted at the same object and are unique. Based on this, a 3D annotation box is used to organize each 3D object using AI_ObjectLabel. Since each training data and each 3D object require many additional attributes to be fully described, many additional attributes to provide a detailed description of the training dataset, training data, labels, etc. were added.

An example of XML encoding of the nuScenes dataset following the TrainingDML-AI UML model can be found in https://github.com/opengeospatial/TrainingDML-AI_SWG/blob/main/use-cases/examples/1.0/nuScenes.xml.

Annex C: Revision History (Informative)

Date	Release	Author	Paragraph modified	Description
2023-12-15	0.1	Peng Yue, Ruixiang Liu, Kaixuan Wang, Yongyi Mi	All	Draft for internal review.
2024-02-26	0.2	Peng Yue, Ruixiang Liu, Carl Reed, Yongyi Mi	Most	Merge edits and comments from Carl Reed.
2024-06-09	0.3	Peng Yue, Ruixiang Liu, Yongyi Mi	Chapter 2, 4, Annex A	Revisions after OAB review and public comments.

Annex D: Bibliography

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[2] Freed, N., 1996. RFC 2046. Multipurpose Internet Mail Extensions (MIME) Part Two: Media Types. https://www.ietf.org/rfc/rfc2046.txt

[3] Portele, C., 2007. OpenGIS Geography Markup Language (GML) Encoding Standard, OGC 07-036. Wayland, MA: Open Geospatial Consortium Inc. https://portal.ogc.org/files/?artifact_id=20509

[4] World Wide Web Consortium. Extensible Markup Language (XML). https://www.w3.org/XML/

[5] World Wide Web Consortium. XML Schema. https://www.w3.org/XML/Schema

[6] ISO, 2019. ISO 19107: 2019. Geographic information — Spatial schema. https://www.iso.org/standard/66175.html

[7] ISO, 2022. ISO 19157-1: 2022. Geographic information — Data quality. https://www.iso.org/standard/78900.html

[8] ISO, 2014. 19115-1:2014, Geographic information — Metadata — Part 1: Fundamentals. https://www.iso.org/standard/53798.html

[9] Landry, T., ed., 2018. OGC Testbed-14: Machine Learning Engineering Report, OGC 18-038r2. Wayland, MA: Open Geospatial Consortium Inc. https://docs.ogc.org/per/18-038r2.html

[10] Meek, S., ed., 2019. OGC Testbed-15: Machine Learning Engineering Report, OGC 19-027r2. Wayland, MA: Open Geospatial Consortium Inc. https://docs.ogc.org/per/19-027r2.html

[11] Schumann, G., ed., 2020. OGC Testbed-16: Machine Learning Training Data Engineering Report, OGC 20-018. Wayland, MA: Open Geospatial Consortium Inc. https://docs.ogc.org/per/20-015r2.html

[12] Yue, P., Shangguan, B., Hu, L., Jiang, L., Zhang, C., Cao, Z., Pan, Y., 2022. Towards a training data model for artificial intelligence in earth observation. International Journal of Geographical Information Science, 1-25. https://doi.org/10.1080/13658816.2022.2087223