基于过采样和卫星图像多特征融合的街区品质评估

城市街区的品质评估是城市规划中的重要步骤之一。针对已有街区品质评估模型中使用的街景图像采集位置和角度不统一造成的图像内容偏差问题，本文提出使用视角统一且易获取的卫星图像代替街景图像。同时考虑多种街区品质测度指标，将非图像型数据与图像型数据进行特征融合，从多角度进行城市街区品质表达，然后针对小样本和数据不平衡场景，结合机器学习中的随机过采样和随机森林（Random Forest，RF），基于融合数据进行城市街区的品质评估。实验结果表明，本研究方法可以有效进行全面的城市街区品质表达，并有效降低过拟合，对比未使用过采样和多特征融合的方法，正确率提高约8个百分点。

Block Quality Evaluation Based on Oversampling and Satellite Image Multi-feature Fusion

The quality evaluation of urban blocks is one of the important steps in urban planning.In order to solve the problem of image content deviation caused by the inconsistent collection position and angle of street view image used in the existing block quality evaluation model, this article proposes to use the satellite image with unified perspective and easy access to replace the street view image.At the same time, considering a variety of block quality measurement indicators, the characteristics of non image data and image data are fused, and the quality of urban blocks is expressed from multiple perspectives.Then, for small samples and data imbalance scenarios, combined with random oversampling and Random Forest (RF) in machine learning, the quality of urban blocks is evaluated based on the fusion data.The experimental results show that this research method can effectively carry out a comprehensive urban block quality expression and effectively reduce over fitting.Compared with the methods without over sampling and multi-feature fusion, the accuracy rate is increased by about 8 percentage points.