GeoAI Training Data: Model, Quality, and Services

YUE Peng; LIU Ruixiang; SHANGGUAN Boyi; CAO Zhipeng; LIU Shuaiqi; XU Hanwen

doi:10.13203/j.whugis20230125

Volume 48 Issue 10

Oct. 2023

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Geomatics and Information Science of Wuhan University > 2023 > 48(10): 1616-1631. > DOI: 10.13203/j.whugis20230125

YUE Peng, LIU Ruixiang, SHANGGUAN Boyi, CAO Zhipeng, LIU Shuaiqi, XU Hanwen. GeoAI Training Data: Model, Quality, and Services[J]. Geomatics and Information Science of Wuhan University, 2023, 48(10): 1616-1631. DOI: 10.13203/j.whugis20230125

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GeoAI Training Data: Model, Quality, and Services

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School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

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Received Date: April 09, 2023
Available Online: April 19, 2023

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Abstract

The data-driven research paradigm brings a strong demand for training data sharing in geospatial artificial intelligence (GeoAI). The training data content and organization from different GeoAI applications are diverse. A unified information model will lay the foundation for GeoAI training data sharing and interoperability. By analyzing the common features and core attributes of different GeoAI training data, an information model for training data was proposed, and the training data quality elements and evaluation methods were explored. The results provide a reference for development of GeoAI training data stores and sharing services.
- GeoAI,
- training data,
- machine learning,
- data sharing

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