在房地产领域使用卷积神经网络对地块形状的分类
1Department of Real Estate, Kangwon National University, Chuncheon, Gangwon-do, Republic of Korea.
PloS one
|September 19, 2024
概括
这项研究引入了一种自动化方法,用于使用卷积神经网络对地块形状进行分类,达到90.1%的准确性. 这种深度学习方法为土地管理的传统视觉检查提供了更快,更客观的替代方案.
科学领域:
- 地理信息学是一种地理信息学.
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 传统的地块形状分类依赖于视觉检查或基于规则的系统,这些系统是低效的,容易产生偏见.
- 现有的方法耗时且资源密集,限制了它们在土地管理中的可扩展性.
- 需要自动化,客观和高效的方法来分类地块形状.
研究的目的:
- 通过深度学习方法开发和评估用于分类地块形状的自动化系统.
- 在速度,成本和客观性方面解决传统方法的局限性.
- 展示卷积神经网络在微空间土地管理中的潜力.
主要方法:
- 一个卷积神经网络,特别是Xception架构,用于形状分类.
- 从选定的研究区域收集和预处理了土地块的图像数据.
- 该模型在土地图像上进行了训练和测试,以分类它们的形状.
主要成果:
- 开发的Xception模型在相邻的测试区域实现了90.1%的分类精度.
- 该模型表现出高性能,曲线下的面积 (AUC) 为0.96.
- 该研究使用神经网络的输出分数成功量化了形状规律性.
结论:
- 深度学习,特别是卷积神经网络,为自动化地块形状分类提供了强大的工具.
- 拟议的方法为传统分类技术提供了一个快速,准确和客观的替代方案.
- 这项研究开创了深度学习在微空间规模的土地管理中的应用,对各种行业产生影响.
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