Towards a training data model for artificial intelligence in earth observation

• Published in: International journal of geographical information science : IJGIS Vol. 36; no. 11; pp. 2113 - 2137
• Main Authors: Yue, Peng, Shangguan, Boyi, Hu, Lei, Jiang, Liangcun, Zhang, Chenxiao, Cao, Zhipeng, Pan, Yinyin
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.11.2022; Taylor & Francis LLC
• Subjects: Accessibility; Artificial intelligence; Data models; Datasets; Deep learning; earth observation; Infrastructure; interoperability; Machine learning; remote sensing; Spatial data; Training; training data
• ISSN: 1365-8816; 1365-8824; 1362-3087
• DOI: 10.1080/13658816.2022.2087223

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.