Evidence map›Paper›PMID 41545392›Full record

ArticleScientific data2026

GlobalBuildingMap - Unveiling the mystery of global buildings.

Xiao Xiang Zhu, Qingyu Li, Yilei Shi, Yuanyuan Wang, Adam J Stewart, Jonathan Prexl, Fahong Zhang

Abstract read
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Xiao Xiang ZhuChair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany. xiaoxiang.zhu@tum.de.ORCID http://orcid.org/0000-0001-5530-3613
Qingyu LiChair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany.
Yilei ShiSchool of Engineering and Design, Technical University of Munich, Munich, Germany.
Yuanyuan WangChair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany.ORCID http://orcid.org/0000-0002-0586-9413
Adam J StewartChair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany.
Jonathan PrexlChair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany.
Fahong ZhangChair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany.

Funding

Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) 01DD20001EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) ERC-2016-StG-714087
6 · The paper itself

Abstract

Understanding how buildings are distributed globally is crucial to revealing the human footprint on our home planet. This built environment affects local climate, land surface albedo, resource distribution, and many other key factors that influence well-being and human health. Despite this, quantitative and comprehensive data on the distribution and properties of buildings worldwide is lacking. Using a big data analytics approach and nearly 800,000 satellite images, we generated the highest resolution and highest accuracy building map ever created: the GlobalBuildingMap (GBM). A joint analysis of building maps and solar potentials indicates that rooftop solar energy can supply the global energy consumption need at a reasonable cost. Specifically, if solar panels were placed on the roofs of all buildings, they could supply 1.1-3.3 times - depending on the efficiency of the solar device - the global energy consumption in 2020.

Identifiers

PMID41545392
PMCPMC12820115

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.