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连接重复的主观判断和ConvNets用于对直接生活环境的多式评估
Miroslav Despotovic1, David Koch1, Simon Thaler1
1University of Applied Sciences Kufstein Tirol, Kufstein, Tyrol, Austria.
MethodsX
|January 29, 2024
概括
这项研究引入了一种结合人类判断和深度学习的新方法,以评估生活环境质量. 这种方法提高了数据的稳定性,并减少了房地产价格建模中的偏差.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 数据科学数据科学数据科学
背景情况:
- 对城市规划和房地产估值来说,评估直接生活环境的质量至关重要.
- 传统方法通常面临着多式联运数据和数据提取中的潜在偏差的挑战.
- 开发可靠和不太偏见的位置评估方法是正在进行的研究领域.
研究的目的:
- 开发和验证一种使用人类判断和神经网络评估生活环境质量的方法.
- 通过将人类的评估作为基本真相,为享乐模型生成合成数据.
- 为了减少建模困难,提高自动位置评估的可靠性.
主要方法:
- 将人类判断作为基本真理纳入用于合成数据生成的神经网络.
- 利用多个独立的注释器和重复的试验来最大限度地减少偏差和错误差异.
- 在代用享乐模型中测试生成的合成数据,用于房地产价格分析.
主要成果:
- 将重复的主观判断与深度学习联系起来,可靠地确定环境质量评分.
- 拟议的方法扩大了可用于质量评估的信息范围.
- 实验结果显示,总体误差差率降低,结果更强大.
结论:
- 人类判断和深度学习的整合为环境质量评估提供了一种计算效率高,适应性强的方法.
- 该方法适用于重复应用,并且可以转移到更广泛的机器学习环境中.
- 关键的实施因素包括足够的代表性数据,重复评估和对人类判断对房地产价格的影响的可靠推导.
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