OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 2: JSON 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-part2/1.0 Internal reference number of this OGC® document: 24-006r1 Version: 1.0 Editor: Peng Yue, Ruixiang Liu, Boyi Shangguan OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 2: JSON Encoding Standard Copyright notice Copyright © 2025 Open Geospatial Consortium 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. 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Except as contained in this notice, the name of LICENSOR or of any other holder of a copyright in all or part of the Intellectual Property shall not be used in advertising or otherwise to promote the sale, use or other dealings in this Intellectual Property without prior written authorization of LICENSOR or such copyright holder. LICENSOR is and shall at all times be the sole entity that may authorize you or any third party to use certification marks, trademarks or other special designations to indicate compliance with any LICENSOR standards or specifications. This Agreement is governed by the laws of the Commonwealth of Massachusetts. The application to this Agreement of the United Nations Convention on Contracts for the International Sale of Goods is hereby expressly excluded. In the event any provision of this Agreement shall be deemed unenforceable, void or invalid, such provision shall be modified so as to make it valid and enforceable, and as so modified the entire Agreement shall remain in full force and effect. No decision, action or inaction by LICENSOR shall be construed to be a waiver of any rights or remedies available to it. Table of Contents 1. Scope 2. Conformance 3. Normative References 4. Terms and Definitions 4.1. Artificial Intelligence (AI) 4.2. Machine Learning (ML) 4.3. Deep Learning (DL) 4.4. Dataset 4.5. Training Dataset 4.6. Label 4.7. Class 4.8. Task 4.9. Provenance 4.10. Quality 4.11. Earth Observation 4.12. Scene Classification 4.13. Object Detection 4.14. Semantic Segmentation 4.15. Change Detection 4.16. 3D Model Reconstruction 4.17. Generative Model 4.18. JavaScript Object Notation (JSON) 4.19. JSON Schema 4.20. Training Dataset Publisher 5. Conventions 5.1. Identifiers 5.2. Abbreviated Terms 6. Overview 6.1. JavaScript Object Notation 7. Requirements for TrainingDML-AI JSON Encoding 7.1. Requirements Class: Base 7.1.1. Requirements Class: JSON Base Type 7.1.2. Requirements Class: ISO Metadata Type 7.1.3. Requirements Class: ISO Quality Type 7.1.4. Requirements Class: Geospatial Type 7.2. Requirements Class: AI_TrainingDataset 7.3. Requirements Class: AI_TrainingData 7.4. Requirements Class: AI_Task 7.5. Requirements Class: AI_Label 7.6. Requirements Class: AI_Labeling 7.7. Requirements Class: AI_DataQuality 7.8. Requirements Class: AI_TDChangeset Annex A: Abstract Test Suite (Normative) A.1. Introduction A.2. Conformance Class: Base A.2.1. Conformance Class: JSON Base Type A.2.2. Conformance Class: ISO Metadata Type A.2.3. Conformance Class: ISO Quality Type A.2.4. Conformance Class: Geospatial Type A.3. Conformance Class: AI_TrainingDataset A.4. Conformance Class: AI_TrainingData A.5. Conformance Class: AI_Task A.6. Conformance Class: AI_Label A.7. Conformance Class: AI_Labeling A.8. Conformance Class: AI_DataQuality A.9. Conformance Class: AI_TDChangeset Annex B: Example (Informative) B.1. TrainingDataset Encoding Examples B.1.1. WHU-RS19 Dataset B.1.2. DOTA-v1.5 Dataset B.1.3. KITTI 2D Object Detection Dataset B.1.4. GID Dataset B.1.5. Toronto3D Dataset B.1.6. WHU-Building Dataset B.1.7. California Change Detection Dataset B.1.8. WHU MVS Dataset B.1.9. iSAID Dataset B.2. DataQuality Encoding Example B.2.1. WHU-RS19 Data Quality B.3. TDChangeset Encoding Example B.3.1. DOTA-v1.5 Changeset B.4. Non-EO Imagery TrainingDataset Encoding Examples B.4.1. ERA5 Dataset B.4.2. SCIERC Dataset B.4.3. nuScenes Dataset Annex C: Revision History (Informative) Annex D: Bibliography i. Abstract The OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 2: JSON Encoding Standard defines requirements for encoding AI training datasets as JavaScript Object Notation (JSON). JSON is widely used for encoding data in Web-based applications. It consists of sets of objects described by name/value pairs. TrainingDML-AI Part 2 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, JSON 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 Haoru Wu Wuhan University Chenxiao Zhang 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, Kaixuan Wang, Zhipeng Cao, Shuaiqi Liu, Ming Zhao, Hanwen Xu, Haipeng Deng, Baoxin Teng. 1. Scope This OGC TrainingDML - AI Part 2: JSON Encoding Standard defines a JSON encoding for the exchange of training datasets. The TrainingDML - AI Part 2 Standard provides a JSON-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 a JSON encoding for AI training datasets. The standardization target for this Standard is: TrainingDML-AI JSON 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. OGC: OGC 23-008r3, OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part1: Conceptual Model Standard, 2023 IETF: RFC 7159, The JavaScript Object Notation (JSON) Data Interchange Format, 2014 IETF: RFC 7946, The GeoJSON Format, 2016 IETF: RFC 3986, Uniform Resource Identifiers (URI): Generic Syntax, 2005 IETF: RFC 3339, Date and Time on the Internet: Timestamps, 2002 IETF: RFC 2046, Multipurpose Internet Mail Extensions (MIME) Part Two: Media Types, 1996 ISO 19107:2019 Geographic information — Spatial schema ISO 19115-1:2014 Geographic information — Metadata — Part 1: Fundamentals ISO 19157-1:2023 Geographic information — Data quality — Part 1: General requirements 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. JavaScript Object Notation (JSON) is a lightweight, text-based, language-independent syntax for defining data interchange formats. It was derived from the ECMAScript programming language but is programming language independent. JSON defines a small set of structuring rules for the portable representation of structured data. SOURCE: ECMA-404 The JSON data interchange syntax 2nd edition, December 2017 4.19. JSON Schema is a vocabulary that allows you to annotate and validate JSON documents. SOURCE: https://json-schema.org/ 4.20. Training Dataset Publisher refers to the entity or individual responsible for creating and releasing the JSON-based serialization syntax for geospatial training datasets, as defined in the TrainingDML-AI Part 2: JSON 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-2/1.0 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 2: JSON Encoding Standard defines a JSON-based serialization syntax for geospatial training datasets. While other serialization formats are possible, such alternatives are not discussed in this Standard. JSON does not have a formal class model. JSON objects are just sets of properties. However, the JSON encoding described in this Standard features a "type" property on each JSON object. A training dataset document conforming to this Standard is a JSON document whose root value is an AI_TrainingDataset object. 6.1. JavaScript Object Notation JavaScript Object Notation (JSON) is a lightweight, text-based, language-independent data interchange format that defines a small set of formatting rules for the portable representation of structured data. JSON is derived from the object literals of JavaScript, as defined in the ECMAScript Programming Language Standard and can represent four primitive types (strings, numbers, Boolean values, and null) and two structured types (objects and arrays). The ordering of the members or properties of any JSON object is considered irrelevant. Even though JSON is based on a subset of the JavaScript Programming Language it is currently well-supported by nearly all programming languages, including Java, Python, and C#. The JSON format is currently described by two competing standards, RFC7159 and ECMA-404. Both standards documents are consistent, but the latter defines mainly the grammatical syntax where the former provides some additional semantic and security points. When serialized, absent properties are represented by either (a) setting the property value to null, or (b) by omitting the property declaration altogether at the option of the training dataset publisher. These representations are semantically equivalent. If a property has an array value, the absence of any items in that array shall be represented by omitting the property entirely or by setting the value to null. The appropriate interpretation of an omitted or explicitly null value is that no value has been assigned, as opposed to the view that the given value is empty or nil. 7. Requirements for TrainingDML-AI JSON Encoding 7.1. Requirements Class: Base 7.1.1. Requirements Class: JSON Base Type The JSON Base Type requirements class defines the base requirements for JSON encodings, which includes definitions of common types used in the TrainingDML-AI JSON encoding. Requirements class /req/base/jsonbasetype Dependency JSON Requirement 1 /req/base/jsonbasetype/json Requirement 2 /req/base/jsonbasetype/datetime Requirement 3 /req/base/jsonbasetype/namedvalue Requirement 4 /req/base/jsonbasetype/url The first requirement is that a TrainingDML-AI JSON document is a valid JSON document. Requirement 1 /req/base/jsonbasetype/json An instance SHALL be a conformant JSON document, as defined in ECMA-404 JSON has a limited range of built-in types ( http://json.org/ ). The following requirements provide standard JSON representations of additional types required across all requirements within this specification. The DateTime is encoded as a text string. Requirement 2 /req/base/jsonbasetype/datetime Each DateTime value SHALL be encoded as a text string defined in Date and Time on the Internet: Timestamps RFC 3339 Section 5.6 . RFC 3339 is a profile of the ISO 8601 standard for representation of dates and times using the Gregorian calendar. The specification of date and time in any JSON 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 a JSON object with two properties named "key" and "value". Typically, the "key" property represents a unique identifier or name for the value being described, while the "value" property 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/jsonbasetype/namedvalue Each NamedValue value SHALL be encoded as a JSON object with properties "key" and "value", while the value of property "key" is a text string. Examples: a) {"key": "forest", "value": "RGB(0,255,255)"} b) {"key": "precision", "value": 0.8} The URL is encoded as a text string. Requirement 4 /req/base/jsonbasetype/url Each URL value SHALL be encoded as a text string defined in Uniform Resource Identifier (URI): Generic Syntax RFC 3986 Section 4.1 . 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 JSON encoding of ISO metadata types. Requirements class /req/base/isometadatatype Dependency JSON Dependency GeoJSON 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 a JSON object. Requirement 5 /req/base/isometadatatype/band Each MD_Band value SHALL be encoded as a JSON object matching the XML Schema type as defined in: https://schemas.isotc211.org/19115/-1/mrc/1.3.0/content.xsd Examples: a) {"name": [{"code": "red"}]} b) {"name": [{"code": "B4"}]} c) {"boundMax": 690, "boundMin": 630, "boundUnits": "nm"} The EX_Extent is encoded as a GeoJSON bounding box or a JSON Object. Requirement 6 /req/base/isometadatatype/extent Each EX_Extent value SHALL be encoded using the GeoJSON bounding box encoding as defined in The GeoJSON Format RFC 7946 Section 5 , or a JSON object matching the XML Schema type as defined in: https://schemas.isotc211.org/19115/-1/gex/1.3.0/extent.xsd Examples: a) [120.0, 30.0, 130.0, 40.0] b) [120.0, 30.0, 10.0, 130.0, 40.0, 20.0] c) { "geographicElement": [{ "westBoundLongitude": -171.76409, "eastBoundLongitude": -157.86768, "southBoundLatitude": -14.42443, "northBoundLatitude": 21.31573 }] } d) { "geographicElement": [{ "polygon": [{ "exterior": { "LinearRing": { "posList": [51.556272, -0.2803943, 51.5562758, -0.2787397, 51.5556539, -0.278736, 51.5556501, -0.2803906, 51.556272, -0.2803943] } } }] }] } The CI_Citation is encoded as a JSON object. Requirement 7 /req/base/isometadatatype/citation Each CI_Citation value SHALL be encoded as a JSON object matching the XML Schema type as defined in: https://schemas.isotc211.org/19115/-1/cit/1.3.0/citation.xsd Example: {"title": "Open Geospatial Consortium", "alternateTitle": ["OGC"], "identifier": [{"code": "https://portal.ogc.org/files/?artifact_id=104605&version=1"}]} The MD_Scope is encoded as a JSON object. Requirement 8 /req/base/isometadatatype/scope Each MD_Scope value SHALL be encoded as a JSON object matching the XML Schema type as defined in: https://schemas.isotc211.org/19115/-1/mcc/1.3.0/commonClasses.xsd Examples: a) { "level": "dataset", "levelDescription": [{ "dataset": "whu_rs19" }] } b) { "level": "attribute", "levelDescription": [{ "attributes": "completeness" }] } c) { "level": "coverage", "extent": [{ "geographicElement": [{ "westBoundLongitude": -171.76409, "eastBoundLongitude": -157.86768, "southBoundLatitude": -14.42443, "northBoundLatitude": 21.31573 }] }] } d) { "level": "feature", "extent": [{ "geographicElement": [{ "polygon": [{ "exterior": { "LinearRing": { "posList": [51.556272, -0.2803943, 51.5562758, -0.2787397, 51.5556539, -0.278736, 51.5556501, -0.2803906, 51.556272, -0.2803943] } } }] }] }] } 7.1.3. Requirements Class: ISO Quality Type The ISO Quality Type requirements class defines the requirements for JSON encoding of ISO quality types. Requirements class /req/base/isoqualitytype Dependency JSON Requirement 9 /req/base/isoqualitytype/element The QualityElement object is encoded as a JSON object with properties shown in Table 1. Requirement 9 /req/base/isoqualitytype/element Each QualityElement value SHALL be encoded as a JSON object with properties shown in Table 1. Table 1. QualityElement properties JSON 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", "measure": { "measureDescription": "Percentage of training samples with inconsistent image format" }, "evaluationMethod": { "evaluationMethodDescription": "Full test method to calculate the percentage of training samples with an inconsistent format" }, "result": [ { "quantitativeResult": { "value": [ 0 ], "valueUnit": "%" } } ] } 7.1.4. Requirements Class: Geospatial Type The Geospatial Type requirements class defines the requirements for JSON encoding of geospatial types. Requirements class /req/base/geospatialtype Dependency JSON Dependency GeoJSON Requirement 10 /req/base/geospatialtype/feature The encoding of one or more features follows the GeoJSON RFC rules for encoding a Feature object, with members “type”, “geometry” and “properties”. A Feature object represents a spatially bounded thing. Every Feature object is a GeoJSON object no matter where it occurs in a GeoJSON text. RFC 7946 Requirement 10 /req/base/geospatialtype/feature Each Feature value SHALL be encoded using the GeoJSON feature encoding defined in The GeoJSON Format RFC 7946 Section 3.2 . Examples of Feature encodings are: a) {"type": "Feature", "geometry": {"type": "Point", "coordinates": [120.0, 30.0]}, "properties": {"class": "station"}} b) {"type": "Feature", "geometry": {"type": "LineString", "coordinates": [[120.0, 30.0], [130.0, 40.0]]}, "properties": {"class": "road"}} c) {"type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[120.0, 30.0], [130.0, 30.0], [125.0, 40.0], [120.0, 30.0]]]}, "properties": {"class": "building"}} 7.2. Requirements Class: AI_TrainingDataset The AI_TrainingDataset requirements class defines a JSON 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 JSON Dependency /req/base/jsonbasetype 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 a JSON object 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 JSON 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 [0..*] 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", "id": "whu_rs19", "name": "WHU-RS19", "description": "Wuhan University-Remote Sensing 19 Categories (WHU-RS19) has 19 classes of remote sensing images scenes obtained from Google Earth", "license": "CC BY-SA 4.0", "amountOfTrainingData": 1013, "createdTime": "2010-01-01", "providers": ["Wuhan University"], "keywords": ["Remote Sensing", "Scene Classification"], "numberOfClasses": 19, "classes": [{"key":"Airport","value":null}, {"key":"Beach","value":null}, {"key":"Bridge","value":null}, {"key":"Commercial","value":null}, {"key":"Desert","value":null}, {"key":"Farmland","value":null}, {"key":"footballField","value":null}, {"key":"Forest","value":null}, {"key":"Industrial","value":null}, {"key":"Meadow","value":null}, {"key":"Mountain","value":null}, {"key":"Park","value":null}, {"key":"Parking","value":null}, {"key":"Pond","value":null}, {"key":"Port","value":null}, {"key":"railwayStation","value":null}, {"key":"Residential","value":null}, {"key":"River","value":null}, {"key":"Viaduct","value":null}], "tasks": [{"type": "AI_EOTask","id": "whu_rs19-task", "description": "Structural high-resolution satellite image indexing", "taskType": "Scene Classification"}], "data": [{"type": "AI_EOTrainingData", "id": "airport_01", "dataSources": [{"title": "googleEarth"}], "dataURL": ["image/Airport/airport_01.jpg"], "labels": [{"type": "AI_SceneLabel", "class": "Airport"}]}, …] } If the optional element AI_MetricsInLiterature is specified, this element is encoded as JSON object 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 JSON 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", "algorithm": "FACNN", "metrics": [{"key": "Overall Accuracy", "value": 0.9881}] } The AI_EOTrainingDataset object is encoded as a JSON object 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 JSON 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", "id": "whu_rs19", "name": "WHU-RS19", "description": "Wuhan University-Remote Sensing 19 Categories (WHU-RS19) has 19 classes of remote sensing images scenes obtained from Google Earth", "license": "CC BY-SA 4.0", "amountOfTrainingData": 1013, "createdTime": "2010-01-01", "providers": ["Wuhan University"], "keywords": ["Remote Sensing", "Scene Classification"], "numberOfClasses": 19, "extent": [-180, -90, 180, 90], "bands": [ {"name": [{"code": "red"}]}, {"name": [{"code": "green"}]}, {"name": [{"code": "blue"}]} ], "imageSize": "6000x7600", "classes": [{"key":"Airport","value":null}, {"key":"Beach","value":null}, {"key":"Bridge","value":null}, {"key":"Commercial","value":null}, {"key":"Desert","value":null}, {"key":"Farmland","value":null}, {"key":"footballField","value":null}, {"key":"Forest","value":null}, {"key":"Industrial","value":null}, {"key":"Meadow","value":null}, {"key":"Mountain","value":null}, {"key":"Park","value":null}, {"key":"Parking","value":null}, {"key":"Pond","value":null}, {"key":"Port","value":null}, {"key":"railwayStation","value":null}, {"key":"Residential","value":null}, {"key":"River","value":null}, {"key":"Viaduct","value":null}], "tasks": [{"type": "AI_EOTask", "id": "whu_rs19-task", "description": "Structural high-resolution satellite image indexing", "taskType": "Scene Classification"}], "data": [{"type": "AI_EOTrainingData", "id": "airport_01", "dataSources": [{"title": "googleEarth"}], "dataURL": ["image/Airport/airport_01.jpg"], "labels": [{"type": "AI_SceneLabel", "class": "Airport"}]}, …] } 7.3. Requirements Class: AI_TrainingData The AI_TrainingData requirements class defines a JSON 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 JSON Dependency /req/base/jsonbasetype Dependency /req/base/isometadatatype Dependency /req/ailabel Dependency /req/ailabeling Dependency /req/aidataquality Requirement 14 /req/aitrainingdata/trainingdata Requirement 15 /req/aitrainingdata/trainingtypecode Requirement 16 /req/aitrainingdata/eotrainingdata The AI_TrainingData object is encoded as a JSON object 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 JSON 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", "id": "airport_01", "dataSources": [{"title": "googleEarth"}], "dataURL": ["image/Airport/airport_01.jpg"], "labels": [{"type": "AI_SceneLabel", "class": "Airport"}] } 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 a JSON object with properties both shown in Table 5 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 JSON 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 dataTime 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", "id": "airport_01", "dataSources": [{"title": "googleEarth"}], "extent": { "geographicElement": [{ "westBoundLongitude": -171.76409, "eastBoundLongitude": -171.56578, "southBoundLatitude": -14.42443, "northBoundLatitude": -14.32568 }] }, "dataTime": ["2002-05-30T09:30:10Z"], "dataURL": ["image/Airport/airport_01.jpg"], "labels": [{"type": "AI_SceneLabel", "class": "Airport"}] } 7.4. Requirements Class: AI_Task The AI_Task requirements class defines a JSON 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 JSON Dependency /req/base/jsonbasetype Requirement 17 /req/aitask/task Requirement 18 /req/aitask/eotask The AI_Task object is encoded as a JSON object 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 JSON 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", "id": "image-indexing-task", "description": "Structural high-resolution satellite image indexing" } The AI_EOTask object is encoded as a JSON object 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 JSON 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", "id": "image-indexing-task", "description": "Structural high-resolution satellite image indexing", "taskType": "Scene Classification" } 7.5. Requirements Class: AI_Label The AI_Label requirements class defines a JSON 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 JSON Dependency /req/base/jsonbasetype 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 a JSON object 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 JSON 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", "isNegative": false, "confidence": 1.0 } The AI_SceneLabel object is encoded as a JSON object with properties as shown in Table 10. Requirement 20 /req/ailabel/scenelabel Each AI_SceneLabel object SHALL implement the properties shown in Table 10. Table 10. AI_SceneLabel properties JSON 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", "class": "Airport" } The AI_ObjectLabel object is encoded as a JSON object 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 JSON 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 [1..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", "class": "Truck", "object": {"type": "Feature", "properties": {"truncated": 0.0, "occluded": 0, "alpha": -1.57}, "geometry": {"type": "Polygon", "coordinates": [[[2257.0, 332.0], [2271.0, 332.0], [2271.0, 350.0], [2257.0, 350.0], [2257.0, 332.0]]]}}, "bboxType": "Horizontal BBox" } The AI_PixelLabel object is encoded as a JSON object 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 JSON 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", "imageURL": ["/label_5classes/GF2_PMS1__L1A0000647767-MSS1_label.tif"], "imageFormat": ["image/tiff; application=geotiff"] } 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 a JSON 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 JSON Dependency /req/base/jsonbasetype 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 a JSON object 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 JSON 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", "id": "0", "scope": { "level": "dataset", "levelDescription": [{ "dataset": "whu_rs19" }] }, "labelers": [{..}], "procedure": {..} } The AI_Labeler object is encoded as a JSON object 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 JSON 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", "id": "0", "name": "Tom" } An AI_LabelingProcedure object is encoded as a JSON object 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 JSON 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", "id": "0", "methods": ["manual"], "tools": ["ArcGIS"] } The AI_LabelingMethodCode is encoded as a text string whose value is one of "manual", "semi-automatic", "automatic" or "unknown". 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 a JSON 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/aidataquality Dependency JSON Dependency /req/base/jsonbasetype Dependency /req/base/isometadatatype Dependency /req/base/isoqualitytype Requirement 28 /req/aidataquality/classbalancedegree The AI_ClassBalanceDegree object is encoded as a JSON object 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 JSON 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", "measure": { "measureDescription": "Balance degree of label classes" }, "evaluationMethod": { "evaluationMethodDescription": "Counting the number of training samples belonging to each class and calculating the balance degree" }, "result": [ { "quantitativeResult": { "value": [ 93.5 ], "valueUnit": "%" } } ] } 7.8. Requirements Class: AI_TDChangeset The AI_TDChangeset requirements class defines a JSON 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 JSON Dependency /req/base/jsonbasetype Dependency /req/tdtrainingdata Requirement 29 /req/aitdchangeset/tdchangeset The AI_TDChangeset object is encoded as a JSON object 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 JSON 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", "id": "changeset-dota_v1.5", "datasetId": "dota_v1.5", "createdTime": "2019-01-01", "changeCount": 9, "modify": [{"type": "AI_EOTrainingData", "id": "P1228", "dataSources": [{"title": "GF"}], "dataURL": ["train/images/P1228.png"], "numberOfLabels": 50, "trainingType": "training", "labels": [{"type": "AI_ObjectLabel", "class": "ship", "object": {"type": "Feature", "properties": {}, "geometry": {"type": "Polygon", "coordinates": [[[2306.0, 729.0], [2330.0, 729.0], [2330.0, 744.0], [2306.0, 744.0], [2306.0,729.0]]]}},"bboxType": "Horizontal BBox"}, …]}] } Annex A: Abstract Test Suite (Normative) A.1. Introduction Conformance is tested using the JSON 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: JSON Base Type Conformance Class http://www.opengis.net/spec/TrainingDML-AI-2/1.0/conf/base/jsonbasetype Requirements class /req/base/jsonbasetype Abstract Test 1 Test ID /conf/base/jsonbasetype/json Requirement /req/base/jsonbasetype/json Test purpose Verify that the document is well-formed JSON. Test method Load the document in a JSON validator. Pass if no errors reported. Fail otherwise. Abstract Test 2 Test ID /conf/base/jsonbasetype/datetime Requirement /req/base/jsonbasetype/datetime Test purpose Verify that JSON instance documents claiming conformance to this specification contain valid DateTime values according to Date and Time on the Internet: Timestamps RFC 3339 Section 5.6 . Test method Inspect the instance document to verify the above requirement. Abstract Test 3 Test ID /conf/base/jsonbasetype/namedvalue Requirement /req/base/jsonbasetype/namedvalue Test purpose Verify that JSON instance documents claiming conformance to this specification validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/namedValue.json . Test method Validate the instance document against the namedValue.json 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 JSON schema specification. Abstract Test 4 Test ID /conf/base/jsonbasetype/url Requirement /req/base/jsonbasetype/url Test purpose Verify that JSON instance documents claiming conformance to this specification contain valid URL values according to Uniform Resource Identifier (URI): Generic Syntax RFC 3986 Section 4.1 . A URL 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-2/1.0/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 JSON objects validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/md_band.json . Test method Validate the instance document against the md_band.json 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 JSON 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 JSON objects validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ex_extent.json . Test method Validate the instance document against the ex_extent.json 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 JSON 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 JSON objects validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ci_citation.json . Test method Validate the instance document against the ci_citation.json 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 JSON 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 JSON objects validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/md_scope.json . Test method Validate the instance document against the md_scope.json 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 JSON schema specification. A.2.3. Conformance Class: ISO Quality Type Conformance Class http://www.opengis.net/spec/TrainingDML-AI-2/1.0/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 JSON objects validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/qualityElement.json . Test method Validate the instance document against the qualityElement.json 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 JSON schema specification. A.2.4. Conformance Class: Geospatial Type Conformance Class http://www.opengis.net/spec/TrainingDML-AI-2/1.0/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 JSON objects validate against the JSON schema specified in https://geojson.org/schema/Feature.json . Test method Validate the instance document against the feature.json 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 JSON 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-2/1.0/conf/aitrainingdataset Requirements class /req/aitrainingdataset Dependency /conf/base/jsonbasetype 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 JSON objects listed in Table 2 validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_trainingDataset.json . Test method Validate the instance document against the ai_trainingDataset.json 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 JSON schema specification. Abstract Test 12 Test ID /conf/aitrainingdataset/metricsinliterature Requirement /req/aitrainingdataset/metricsinliterature Test purpose Verify that instance documents using the AI_MetricsInLiterature JSON objects listed in Table 3 validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_metricsInLiterature.json . Test method Validate the instance document against the ai_metricsInLiterature.json 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 JSON schema specification. Abstract Test 13 Test ID /conf/aitrainingdataset/eotrainingdataset Requirement /req/aitrainingdataset/eotrainingdataset Test purpose Verify that instance documents using the AI_EOTrainingDataset JSON objects listed in Table 2 and Table 4 validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_eoTrainingDataset.json . Test method Validate the instance document against the ai_eoTrainingDataset.json 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 JSON 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-2/1.0/conf/aitrainingdata Requirements class /req/aitrainingdata Dependency /conf/base/jsonbasetype 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 JSON objects listed in Table 5 validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_trainingData.json . Test method Validate the instance document against the ai_trainingData.json 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 JSON schema specification. Abstract Test 15 Test ID /conf/aitrainingdata/trainingtypecode Requirement /req/aitrainingdata/trainingtypecode Test purpose Verify that instance documents using the AI_TrainingTypeCode JSON objects validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_trainingTypeCode.json . Test method Validate the instance document against the ai_trainingTypeCode.json 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 JSON schema specification. Abstract Test 16 Test ID /conf/aitrainingdata/eotrainingdata Requirement /req/aitrainingdata/eotrainingdata Test purpose Verify that instance documents using the AI_EOTrainingData JSON objects listed in Table 5 and Table 6 validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_eoTrainingData.json . Test method Validate the instance document against the ai_eoTrainingData.json 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 JSON 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-2/1.0/conf/aitask Requirements class /req/aitask Dependency /conf/base/jsonbasetype Abstract Test 17 Test ID /conf/aitask/task Requirement /req/aitask/task Test purpose Verify that instance documents using the AI_Task JSON objects listed in Table 7 validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_task.json . Test method Validate the instance document against the ai_task.json 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 JSON schema specification. Abstract Test 18 Test ID /conf/aitask/eotask Requirement /req/aitask/eotask Test purpose Verify that instance documents using the AI_EOTask JSON objects listed in Table 7 and Table 8 validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_eoTask.json . Test method Validate the instance document against the ai_eoTask.json 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 JSON 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-2/1.0/conf/ailabel Requirements class /req/ailabel Dependency /conf/base/jsonbasetype 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 JSON objects listed in Table 9 validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_label.json . Test method Validate the instance document against the ai_label.json 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 JSON schema specification. Abstract Test 20 Test ID /conf/ailabel/scenelabel Requirement /req/ailabel/scenelabel Test purpose Verify that instance documents using the AI_SceneLabel JSON objects listed in Table 10 validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_sceneLabel.json . Test method Validate the instance document against the ai_sceneLabel.json 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 JSON schema specification. Abstract Test 21 Test ID /conf/ailabel/objectlabel Requirement /req/ailabel/objectlabel Test purpose Verify that instance documents using the AI_ObjectLabel JSON objects listed in Table 11 validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_objectLabel.json . Test method Validate the instance document against the ai_objectLabel.json 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 JSON schema specification. Abstract Test 22 Test ID /conf/ailabel/pixellabel Requirement /req/ailabel/pixellabel Test purpose Verify that instance documents using the AI_PixelLabel JSON objects listed in Table 12 validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_pixelLabel.json . Test method Validate the instance document against the ai_pixelLabel.json 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 JSON schema specification. Abstract Test 23 Test ID /conf/ailabel/imageformatcode Requirement /req/ailabel/imageformatcode Test purpose Verify that instance documents using the AI_ImageFormatCode JSON objects 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-2/1.0/conf/ailabeling Requirements class /req/ailabeling Dependency /conf/base/jsonbasetype 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 JSON objects listed in Table 13 validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_labeling.json . Test method Validate the instance document against the ai_labeling.json 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 JSON schema specification. Abstract Test 25 Test ID /conf/ailabeling/labeler Requirement /req/ailabeling/labeler Test purpose Verify that instance documents using the AI_Labeler JSON objects listed in Table 14 validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_labeler.json . Test method Validate the instance document against the ai_labeler.json 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 JSON schema specification. Abstract Test 26 Test ID /conf/ailabeling/labelingprocedure Requirement /req/ailabeling/labelingprocedure Test purpose Verify that instance documents using the AI_LabelingProcedure JSON objects listed in Table 15 validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_labelingProcedure.json . Test method Validate the instance document against the ai_labelingProcedure.json 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 JSON schema specification. Abstract Test 27 Test ID /conf/ailabeling/labelingmethodcode Requirement /req/ailabeling/labelingmethodcode Test purpose Verify that instance documents using the AI_LabelingMethodCode JSON objects validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_labelingMethodCode.json . Test method Validate the instance document against the ai_LabelingMethodCode.json 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 JSON 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-2/1.0/conf/aidataquality Requirements class /req/aidataquality Dependency /conf/base/jsonbasetype 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 JSON objects listed in Table 16 validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_classBalanceDegree.json . Test method Validate the instance document against the ai_classBalanceDegree.json 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 JSON 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-2/1.0/conf/aitdchangeset Requirements class /req/aitdchangeset Dependency /conf/base/jsonbasetype 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 JSON objects listed in Table 17 validate against the JSON schema specified in http://schemas.opengis.net/trainingdml-ai/part2/1.0/ai_tdChangeset.json . Test method Validate the instance document against the ai_tdChangeset.json 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 JSON 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 JSON 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.json . 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 JSON 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.json . 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 JSON 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.json . 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 JSON 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.json . 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 JSON 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.json . 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 JSON 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.json . 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 JSON 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.json . 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 JSON 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.json . 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 JSON 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.json . 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.json . 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.json . 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 JSON 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.json . 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 JSON 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/SCIERC.json . 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 JSON 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.json . Annex C: Revision History (Informative) Date Release Author Paragraph modified Description 2023-07-28 0.1 Peng Yue, Ruixiang Liu, Boyi Shangguan All Draft for internal review. 2023-12-15 0.2 Peng Yue, Ruixiang Liu, Jim Antonisse Most Revisions based on comments from Jim Antonisse. 2024-02-26 0.3 Peng Yue, Ruixiang Liu, Carl Reed Most Merge edits and comments from Carl Reed. 2024-06-09 0.4 Peng Yue, Ruixiang Liu Chapter 2, 4, Annex A Revisions after OAB review and public comments. Annex D: Bibliography [1] Yue, P., ed., 2023. OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part1: Conceptual Model Standard, OGC 23-008r3. Wayland, MA: Open Geospatial Consortium Inc. https://docs.ogc.org/is/23-008r3/23-008r3.html [2] Freed, N., 1996. RFC 2046. Multipurpose Internet Mail Extensions (MIME) Part Two: Media Types. https://www.ietf.org/rfc/rfc2046.txt [3] Klyne, G., 2002. RFC 3339. Date and Time on the Internet: Timestamps. http://www.ietf.org/rfc/rfc3339.txt [4] Berners-Lee, T., 2005. RFC 3986. Uniform Resource Identifier (URI): Generic Syntax. http://www.ietf.org/rfc/rfc3986.txt [5] Bray, T., ed., 2014. RFC 7159. The JavaScript Object Notation (JSON) Data Interchange Format. http://www.ietf.org/rfc/rfc7159.txt [6] Butler, H., ed., 2016. RFC 7946. The GeoJSON Format. http://www.ietf.org/rfc/rfc7946.txt [7] ISO, 2019. ISO 19107: 2019. Geographic information — Spatial schema. https://www.iso.org/standard/66175.html [8] ISO, 2022. ISO 19157-1: 2022. Geographic information — Data quality. https://www.iso.org/standard/78900.html [9] ISO, 2014. 19115-1:2014, Geographic information — Metadata — Part 1: Fundamentals. https://www.iso.org/standard/53798.html [10] 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 [11] 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 [12] 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 [13] 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 Last updated 2025-02-10 12:50:21 -0500