从Sanborn消防保险地图中创建历史城市社区的建筑层次的三维数字模型,使用机器学习
Yue Lin1,2, Jialin Li3, Adam Porr4
1Department of Geography, The Ohio State University, Columbus, Ohio, United States of America.
PloS one
|June 28, 2023
概括
本研究引入了一种机器学习工作流程,自动从历史的桑博恩地图中提取建筑信息. 该方法准确地识别建筑物的足迹和属性,有助于城市变化分析和3D可视化.
科学领域:
- 城市规划 城市规划
- 历史地理 历史地理
- 计算机视觉 计算机视觉
背景情况:
- 桑博恩消防保险地图提供了详细的历史建筑数据,对城市研究至关重要.
- 由于地图的复杂性和缺乏自动化方法,提取这些数据具有挑战性.
研究的目的:
- 开发一个可扩展的机器学习工作流程,用于从Sanborn地图中自动地提取建筑足迹和财产.
- 为了使历史悠久的城市社区的3D可视化,并为城市转型的研究提供信息.
主要方法:
- 利用机器学习算法在Sanborn地图上识别建筑足迹和相关属性.
- 开发了一个可扩展的工作流程,以实现高效和有效的数据提取.
- 将方法应用于俄俄州哥伦布的桑博恩地图,重点关注受1960年代高速公路建设影响的地区.
主要成果:
- 在提取建筑层次信息方面实现了高准确度.
- 在建筑足迹和建筑材料方面表现出F-1分数0.9的F-1.
- 在建筑物利用率和楼层数量方面获得了超过0.7的F-1评分.
- 成功可视化了高速公路前的城市景观.
结论:
- 开发的机器学习工作流有效地从Sanborn地图中提取有价值的建筑级数据.
- 这种方法有助于创建3D可视化,并加强对历史城市变化的研究.
- 该方法为了解高速公路建设等城市发展项目的影响提供了准确的数据.
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