Towards a training data model for artificial intelligence in earth observation. Issue 11 (2nd November 2022)

• Record Type: Journal Article
• Authors: Yue, Peng; Shangguan, Boyi; Hu, Lei; Jiang, Liangcun; Zhang, Chenxiao; Cao, Zhipeng; Pan, Yinyin
• Is Part Of: International journal of geographical information science. Volume 36:Issue 11(2022)
• Page Start: 2113
• Page End: 2137
• Publication Date: 2022-11-02
• Subjects: Artificial intelligence -- machine learning -- interoperability -- earth observation -- remote sensing -- training data; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2022.2087223
• Languages: English
• ISSNs: 1365-8816

Abstract

Abstract: 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.