Evidence map›Paper›PMID 39695260›Full record

ArticleScientific data2024

A global product of 150-m urban building height based on spaceborne lidar.

Xiao Ma, Guang Zheng, Chi Xu, L Monika Moskal, Peng Gong, Qinghua Guo, Huabing Huang, Xuecao Li, Xinlian Liang, Yong Pang and 5 more

Abstract read
In one paragraph

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

15 authors.

Xiao MaSchool of Life Sciences, Nanjing University, Nanjing, 210023, China.
Guang ZhengInternational Institute for Earth System Sciences, Nanjing University, Nanjing, 210023, China. zhengguang@nju.edu.cn.
Chi XuSchool of Life Sciences, Nanjing University, Nanjing, 210023, China. xuchi@nju.edu.cn.
L Monika MoskalSchool of Environmental and Forest Sciences, University of Washington, Seattle, WA, 98195, USA.
Peng GongDepartment of Geography, Urban Systems Institute, The University of Hong Kong, Hong Kong, 999077, China.ORCID http://orcid.org/0000-0003-1513-3765
Qinghua GuoInstitute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing, 100871, China.ORCID http://orcid.org/0000-0002-1065-0838
Huabing HuangSchool of Geospatial Engineering and Science, Sun Yat-Sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China.ORCID http://orcid.org/0000-0001-6253-8437
Xuecao LiCollege of Land Science and Technology, China Agricultural University, Beijing, 100083, China.ORCID http://orcid.org/0000-0002-6942-0746
Xinlian LiangState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China.
Yong PangInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China.
Cheng WangKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
Huan XieCollege of Surveying and Geo-Informatics, and Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai, 200092, China.
Bailang YuSchool of Geographic Sciences, East China Normal University, Shanghai, 200241, China.ORCID http://orcid.org/0000-0001-5628-0003
Bo ZhaoDepartment of Geography, University of Washington, Seattle, WA, 98195, USA.
Yuyu ZhouDepartment of Geography, Urban Systems Institute, The University of Hong Kong, Hong Kong, 999077, China.ORCID http://orcid.org/0000-0003-1765-6789

Funding

National Natural Science Foundation of China (National Science Foundation of China) 42171340
6 · The paper itself

Abstract

Urban building height, as a fundamental 3D urban structural feature, has far-reaching applications. However, creating readily available datasets of recent urban building heights with fine spatial resolutions and global coverage remains a challenging task. Here, we provide a 150-m global urban building heights dataset around 2020 by combining the spaceborne lidar (Global Ecosystem Dynamics Investigation, GEDI), multi-sourced data (Landsat-8, Sentinel-2, and Sentinel-1), and topographic data. The validation results revealed that the GEDI-estimated building height samples were effective compared to the reference data (Pearson's r = 0.81, RMSE = 3.58 m). The mapping product also demonstrated good performance, as indicated by its strong correlation with the reference data (Pearson's r = 0.71, RMSE = 4.73 m). Compared with the currently existing datasets, it holds the ability to provide a spatial resolution (150 m) with a great level of inherent details about the spatial heterogeneity and flexibility of updating using the GEDI samples as inputs. This product will boost future urban studies across many fields, including environmental, ecological, and social sciences.

Identifiers

PMID39695260
PMCPMC11655538

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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.