The main purpose of this study is to propose an interoperable land valuation data model for residential properties as an extension of the national geographic data infrastructure (GDI) and to make mass valuation process applicable with the use of machine learning approach. As an example, random forest (RF) ensemble algorithm was implemented in Pendik district of Istanbul to evaluate the prediction performance by using thematic datasets compatible with the data model. This study provides a methodology for various urban applications and robustness of the algorithm increases the prediction of the real estate values with the use of qualified datasets.

中文翻译：

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在作为国家 GDI 扩展的可互操作土地估价数据模型上实施大规模估价应用程序

本研究的主要目的是为住宅物业提出一个可互操作的土地估价数据模型，作为国家地理数据基础设施 (GDI) 的扩展，并使大规模估价过程适用于使用机器学习方法。例如，在伊斯坦布尔的 Pendik 区实施了随机森林 (RF) 集成算法，通过使用与数据模型兼容的专题数据集来评估预测性能。这项研究为各种城市应用提供了一种方法，算法的稳健性通过使用合格的数据集增加了对房地产价值的预测。