In order to build a virtual urban planning model and improve the effect of urban planning, this paper builds a virtual urban planning design model based on GIS big data technology and machine learning algorithms, and proposes a solution that combines multiple features. With the development of polarized SAR in the direction of high resolution, a single feature often cannot fully express the detailed information of ground objects, resulting in poor classification results and low accuracy. The combination of multiple features can express feature information well. In addition, this paper uses the ELM method to plan SAR ground object classification, uses an extreme learning machine classification algorithm with fast learning speed and good classification effect, and uses ELM as a classifier. Finally, this paper designs experiments to explore the performance of the model constructed in this paper from two aspects: detection accuracy and planning score. The research results show that the model constructed in this paper meets the expected goals.

中文翻译：

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基于GIS大数据和机器学习的城市规划虚拟设计。

为了构建虚拟的城市规划模型并提高城市规划的效果，本文建立了基于GIS大数据技术和机器学习算法的虚拟城市规划设计模型，并提出了一种融合多种功能的解决方案。随着极化SAR向着高分辨率方向发展，单一特征常常不能完全表达地面物体的详细信息，导致分类效果差，精度低。多个特征的组合可以很好地表达特征信息。另外，本文采用ELM方法来规划SAR地面物体的分类，采用学习速度快，分类效果好的极限学习机分类算法，并以ELM作为分类器。最后，本文设计了实验，从检测准确性和计划得分两个方面探讨了本文构建模型的性能。研究结果表明，本文构建的模型符合预期目标。