Stipek Clinton, Hauser Taylor, Adams Daniel, Epting Justin, Brelsford Christa, Moehl Jessica, Dias Philipe, Piburn Jesse, Stewart Robert
Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA.
Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Sci Rep. 2024 Aug 12;14(1):18651. doi: 10.1038/s41598-024-66467-2.
As cities continue to grow globally, characterizing the built environment is essential to understanding human populations, projecting energy usage, monitoring urban heat island impacts, preventing environmental degradation, and planning for urban development. Buildings are a key component of the built environment and there is currently a lack of data on building height at the global level. Current methodologies for developing building height models that utilize remote sensing are limited in scale due to the high cost of data acquisition. Other approaches that leverage 2D features are restricted based on the volume of ancillary data necessary to infer height. Here, we find, through a series of experiments covering 74.55 million buildings from the United States, France, and Germany, it is possible, with 95% accuracy, to infer building height within 3 m of the true height using footprint morphology data. Our results show that leveraging individual building footprints can lead to accurate building height predictions while not requiring ancillary data, thus making this method applicable wherever building footprints are available. The finding that it is possible to infer building height from footprint data alone provides researchers a new method to leverage in relation to various applications.
随着全球城市持续发展，描述建成环境对于了解人口、预测能源使用、监测城市热岛效应影响、防止环境退化以及规划城市发展至关重要。建筑物是建成环境的关键组成部分，目前全球层面缺乏建筑物高度数据。当前利用遥感开发建筑物高度模型的方法因数据采集成本高昂而在规模上受到限制。其他利用二维特征的方法则因推断高度所需辅助数据的数量而受到限制。在此，我们通过一系列涵盖美国、法国和德国7455万栋建筑物的实验发现，利用占地面积形态数据，以95%的准确率推断出的建筑物高度与真实高度相差在3米以内是可行的。我们的结果表明，利用单个建筑物占地面积可实现准确的建筑物高度预测，同时无需辅助数据，因此该方法在有建筑物占地面积数据的任何地方都适用。仅从占地面积数据就能推断建筑物高度这一发现为研究人员在各种应用中提供了一种新的可用方法。
