地理人工智能样本:模型、质量与服务

GeoAI Training Data: Model, Quality, and Services

数据驱动的研究范式对地理人工智能(geospatial artificial intelligence, GeoAI)样本数据共享提出了强烈需求。不同的GeoAI应用样本数据内容和组织形式多样,如何构建统一的信息模型,是GeoAI样本数据共享与互操作的前提。通过分析不同GeoAI样本数据的公共特征与核心属性,提出了样本数据的共享信息模型,探讨了样本数据质量指标体系和评估方法,为GeoAI样本数据建库与共享服务提供了参考。

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.