Wuhan University receives OGC Community Impact Award
The award recognizes OGC members who, through exceptional leadership, volunteerism, collaboration, and investment, have positively impacted the geospatial community.
Training Data Markup Language for Artificial Intelligence
Overview
The Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Standard aims to develop the UML model and encodings for geospatial machine learning training data. Training data plays a fundamental role in Earth Observation (EO) Artificial Intelligence Machine Learning (AI/ML), especially Deep Learning (DL). It is used to train, validate, and test AI/ML models. This Standard defines a UML model and encodings consistent with the OGC Standards baseline to exchange and retrieve the training data in the Web environment.
The TrainingDML-AI Standard provides detailed metadata for formalizing the information model of training data. This includes but is not limited to the following aspects:
- How the training data is prepared, such as provenance or quality;
- How to specify different metadata used for different ML tasks such as scene/object/pixel levels;
- How to differentiate the high-level training data information model and extended information models specific to various ML applications; and
- How to introduce external classification schemes and flexible means for representing ground truth labeling.
Downloads
| Document title | Version | OGC Doc No. | Type |
|---|---|---|---|
| OGC Training Data Markup Language for Artificial Intelligence (TrainingDML-AI) Part 1: Conceptual Model Standard | 1.0 | 23-008r3 | IS |
Official model files and encoding schemas
Related links
No Results Found.
OGC Standards
- 3D Tiles
- 3dP
- ARML2.0
- Cat: ebRIM App Profile: Earth Observation Products
- Catalogue Service
- CDB
- CityGML
- CityJSON
- Coordinate Transformation
- CoverageJSON
- EO-GeoJSON
- Filter Encoding
- GML in JPEG 2000
- GeoAPI
- GeoPackage
- GeoPose
- GeoSciML
- GeoSPARQL
- Geography Markup Language
- GeoRSS
- GeoXACML
- Geospatial User Feedback (GUF)
- GeoTiff
- GroundwaterML
- HDF5
- I3S
- Indoor Mapping Data Format (IMDF)
- IndoorGML
- KML
- LandInfra/InfraGML
- LAS
- Location Services (OpenLS)
- Moving Features
- NetCDF
- Observations, Measurements, and Samples
- OGC API – EDR
- OGC API – Common
- OGC API – Features
- OGC API – Processes
- OGC API – Tiles
- Open GeoSMS
- OpenMI
- OpenSearch
- Ordering Services Framework for Earth Observation Products
- OWS Context
- OWS Security
- PipelineML
- PubSub
- PUCK
- SWE Common Data Model
- SWE Service Model
- Sensor Model Language
- Sensor Observation Service
- Sensor Planning Service
- SensorThings
- Semantic Sensor Network (SSN)
- STAplus
- Symbology Core
- Simple Features
- Simple Features CORBA
- Simple Features OLE/COM
- Simple Features SQL
- Styled Layer Descriptor
- Symbology Encoding
- Table Joining Service
- Time Ontology in OWL
- TimeseriesML (tsml)
- TrainingDML for AI
- Two Dimensional Tile Matrix Set
- WaterML
- Web Coverage Processing Service
- Web Coverage Service
- Web Feature Service
- Web Map Context
- Web Map Service
- Web Map Tile Service
- Web Processing Service
- Web Service Common
- WKT CRS
