機械学習で世界の建物の高さ予測が容易に(Global building height prediction made easy with machine learning)

2024-12-11 オークリッジ国立研究所(ORNL)

＜関連情報＞

• https://www.ornl.gov/news/global-building-height-prediction-made-easy-machine-learning
• https://www.nature.com/articles/s41598-024-66467-2

フットプリント形状データから建物の高さを推測する Inferring building height from footprint morphology data

Clinton Stipek,Taylor Hauser,Daniel Adams,Justin Epting,Christa Brelsford,Jessica Moehl,Philipe Dias,Jesse Piburn & Robert Stewart
Scientific Reports  Published:12 August 2024
DOI:https://doi.org/10.1038/s41598-024-66467-2

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.