ArticleScientific data2024
An OpenStreetMap derived building classification dataset for the United States.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Access to Essential Services: A Critical Factor for Predicting Recovery and Quantifying Resilience.Risk analysis : an official publication of the Society for Risk Analysis · 2026Article
- From Footprints to Functions: A Comprehensive Global and Semantic Building Footprint Dataset.Scientific data · 2025Article
- High-resolution estimates of the US population in fluvial or coastal flood hazard areas.Scientific data · 2025Article
- GHS-OBAT: Global, open building attribute data reporting age, function, height and compactness at footprint level.Data in brief · 2025Article
- A visitor-enriched census in the U.S. cities using large-scale mobile positioning data.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Building classification 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 presents a comprehensive dataset by providing residential/non-residential building classification covering the entire United States. We developed a dataset of building types based on building footprints and the available OpenStreetMap information. The dataset is validated using authoritative ground truth data for select counties in the U.S., which shows a high precision for non-residential building classification and a high recall for residential buildings. In addition to the building classifications, this dataset includes detailed information on the OpenStreetMap data used in the classification process. 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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.