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

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Xiao Ma, Guang Zheng, Chi Xu, L Monika Moskal, Peng Gong, Qinghua Guo, Huabing Huang, Xuecao Li, Xinlian Liang, Yong Pang, Cheng Wang, Huan Xie, Bailang Yu, Bo Zhao, Yuyu Zhou
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引用次数: 0

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.

基于星载激光雷达的全球150米城市建筑高度产品。
城市建筑高度作为城市三维结构的基本特征,有着深远的应用前景。然而,创建具有良好空间分辨率和全球覆盖范围的近期城市建筑高度的现成数据集仍然是一项具有挑战性的任务。本文结合星载激光雷达(全球生态系统动力学调查,GEDI)、多源数据(Landsat-8、Sentinel-2和Sentinel-1)和地形数据,提供了2020年左右150米的全球城市建筑高度数据集。验证结果表明,与参考数据相比,gedi估算的建筑高度样本是有效的(Pearson’s r = 0.81, RMSE = 3.58 m)。该制图产品也表现出良好的性能,与参考数据具有较强的相关性(Pearson’s r = 0.71, RMSE = 4.73 m)。与目前现有的数据集相比,它能够提供一个空间分辨率(150米),并具有关于空间异质性的大量固有细节和使用GEDI样本作为输入进行更新的灵活性。该产品将推动未来在许多领域的城市研究,包括环境、生态和社会科学。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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