A segmented approach to modeling building height: Delineating high-rise and low-rise buildings for enhanced height estimation

Clinton Stipek, Daniel Adams, Philipe Dias, Taylor Hauser, Viswadeep Lebakula, Alexander Sorokine, Justin Epting, Jessica Moehl, Robert Stewart

Research output: Contribution to journalArticlepeer-review

Abstract

Understanding building height is imperative to the overall study of energy efficiency, population distribution, urban morphologies, emergency response, among others. Currently, existing approaches for modeling building height at scale are hindered by two pervasive issues. First, there is no consistent approach to quantify what a high-rise building is at a macro scale, leaving researchers unable to accurately compare results across geographies and domains. Second, high-rise buildings represent a small fraction of the built environment, implying data imbalance challenges that negatively affect current approaches. This is a problem of practical relevance since information on high-rise buildings is important for studies on urban heat islands, population dynamics, and pollution dispersion. Here, we introduce a novel approach to map building height which first identifies two distinct distributions within the built environment, with one being composed of low-rise buildings and one composed of high-rise buildings. We then develop an ensemble scheme where discrete specialist models are trained for each subset of low-rise buildings and high-rise buildings to infer building height from morphology features. For experiments mapping heights of 4.85 million buildings in Japan, we show an increase of 34 % in accuracy within 3m error when compared to the current state-of-the-art when modeling high-rise buildings, which based on KNN experimentation we define as any building >12m. Our findings show that such an ensemble framework outperforms the current state-of-the-art approaches, which is especially relevant in relation to inferring height for high-rise buildings, a prominent issue of existing approaches for mapping the built environment.

Original languageEnglish
Article number102287
JournalComputers, Environment and Urban Systems
Volume119
DOIs
StatePublished - Jul 2025

Funding

We would like to thank Dr. Cecilia Clark and Dr. James D. Gaboardi for their help and assistance. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE) . The US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).

Keywords

  • Building height
  • Ensemble model
  • Machine learning
  • Urban environment

Fingerprint

Dive into the research topics of 'A segmented approach to modeling building height: Delineating high-rise and low-rise buildings for enhanced height estimation'. Together they form a unique fingerprint.

Cite this