Aerospace engineering

Interoperability

Earth observation

Data model (GIS)

Data mining

Geodesy

World Wide Web

Engineering

Benchmark (surveying)

Artificial intelligence

Satellite

Geography

Programming language

Data science

Discoverability

Open data

Computer science

Geospatial analysis

Documentation

Remote sensing

Data modeling

Database

Metadata

International Journal of Geographical Information Science

Artificial Intelligence Machine Learning (AI/ML), in particular Deep Learning (DL), is reorienting and transforming Earth Observation (EO). A consistent data model for delivery of training data will support the FAIR data principles (findable, accessible, interoperable, reusable) and enable Web-based use of training data in a spatial data infrastructure (SDI). Existing training datasets, including open source benchmark datasets, are usually packaged into public or personal repositories and lack discoverability and accessibility. Moreover, there is no unified method to describe the training data. Here we propose a training data model for AI in EO to allow documentation, storage, and sharing of geospatial training data in a distributed infrastructure. We present design rationales, information models, and an encoding method. Several scenarios illustrate the intended uses and benefits for EO DL applications in an open Web environment. The relationship with Open Geospatial Consortium (OGC) standards is also discussed, as is the impact on an AI-ready SDI.

Towards a training data model for artificial intelligence in earth observation