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.

本研究解决了建筑类型信息匮乏的问题，该信息对人口估计、交通规划、城市规划和应急响应至关重要。我们采用无监督机器学习方法对美国的建筑进行分类，并依据权威的数据进行了验证，结果显示在非住宅建筑分类中具有高精度，在住宅建筑分类中具有高召回率。该研究产生的数据集包含了67,705,475栋建筑，预计将为城市及交通规划等科学研究提供重要价值。

从OpenStreetMap数据中提取美国建筑类型