用无监督方法从开放街图数据中分类全美6700万栋建筑类型。
Extracting the U.S. building types from OpenStreetMap data
- 基于建筑轮廓和开放街图信息，采用无监督学习自动分类建筑类型。
- 分类结果在非住宅建筑上精度高，住宅建筑召回率高。
- 识别出删去车库和棚屋可提升分类质量，缺漏元数据是主要错误原因。
建筑类型信息对人口估算、交通规划、城市规划和应急响应至关重要，但常难以获取。本文通过整合美国全境的建筑轮廓与开放街图（OpenStreetMap）数据，构建了一个包含67,705,475栋建筑的全面建筑类型分类数据集。提出并使用无监督机器学习方法，基于建筑轮廓及可用的开放街图信息进行分类。在部分县区使用权威地面真值数据验证，结果显示非住宅建筑分类精度高，住宅建筑召回率高。研究还发现，移除车库和棚屋等附属结构可显著提升分类质量；而误分类主要源于开放街图中缺失或稀疏的元数据。该数据集有望为城市与交通规划等领域的科研人员提供支持。
原文摘要 · Abstract (English)
Building type information is crucial for population estimation, traffic planning, urban planning, and emergency response applications. Although essential, such data is often not readily available. To alleviate this problem, this work creates a comprehensive dataset by providing residential/non-residential building classification covering the entire United States. We propose and utilize an unsupervised machine learning method to classify building types based on building footprints and available OpenStreetMap information. The classification result is validated using authoritative ground truth data for select counties in the U.S. The validation shows a high precision for non-residential building classification and a high recall for residential buildings. We identified various approaches to improving the quality of the classification, such as removing sheds and garages from the dataset. Furthermore, analyzing the misclassifications revealed that they are mainly due to missing and scarce metadata in OSM. A major result of this work is the resulting dataset of classifying 67,705,475 buildings. We hope that this data is of value to the scientific community, including urban and transportation planners.
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