PDF(Pubmed)
Abstract：
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.
摘要：
随着全球城市的不断发展，表征建筑环境对于理解人类种群至关重要，预测能源使用量，监测城市热岛影响，防止环境恶化，和城市发展规划。建筑物是建筑环境的关键组成部分，目前缺乏全球范围内的建筑物高度数据。由于数据采集的高成本,用于开发利用遥感的建筑物高度模型的当前方法在规模上受到限制。利用2D特征的其他方法基于推断高度所需的辅助数据的量而受到限制。这里,我们发现,通过一系列覆盖美国7455万栋建筑的实验，法国,德国,这是可能的，95%的准确率,使用足迹形态数据推断实际高度3m以内的建筑高度。我们的结果表明，利用单个建筑物的足迹可以导致准确的建筑物高度预测，同时不需要辅助数据，因此，这种方法适用于任何有建筑足迹的地方。仅从足迹数据就可以推断建筑物高度的发现为研究人员提供了一种新的方法来利用各种应用。
