Mapping 10-m Industrial Lands across 1000+ Global Large Cities, 2017-2023.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-16 DOI:10.1038/s41597-025-04604-w
Cheolhee Yoo, Yuhan Zhou, Qihao Weng
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Abstract

Industrial lands, as a key component of economic development, pose great environmental challenges, which underscores the need for close global monitoring to support sustainable urban development. Despite this importance, global city-level maps of industrial land use, especially over multiple years, have been lacking. Here, we present a 10-m resolution global dataset tracking industrial land use in 1,093 large cities (area 100 km² or more) from 2017 to 2023. Using multisource geospatial data and machine learning, the dataset achieves a high overall accuracy of 91.87% to 92.21% across the seven-year period, aligning well with official city maps. We further validated its reliability by computing industrial land area per capita for 1,093 cities, which correlated strongly with per capita CO2 emissions (r = 0.72). These maps offer a valuable tool for tracking industrial land use changes and assessing their impact on urban ecosystems. The dataset is a critical resource for studying the links between industrialization, urbanization, and environmental sustainability while providing insights to policymakers on balancing economic and environmental priorities.

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2017-2023年全球1000多个大城市10米工业用地地图。
工业用地作为经济发展的一个关键组成部分,构成了巨大的环境挑战,这突出表明需要进行密切的全球监测,以支持可持续的城市发展。尽管如此,全球城市级工业用地地图,特别是多年来的工业用地地图,一直缺乏。在这里,我们提供了一个10米分辨率的全球数据集,跟踪了2017年至2023年1,093个大城市(面积为100平方公里或以上)的工业用地使用情况。利用多源地理空间数据和机器学习,该数据集在7年期间实现了91.87%至92.21%的高整体精度,与官方城市地图很好地吻合。我们通过计算1093个城市的人均工业用地面积进一步验证了其可靠性,人均工业用地面积与人均二氧化碳排放量呈强相关(r = 0.72)。这些地图为跟踪工业用地变化和评估其对城市生态系统的影响提供了宝贵的工具。该数据集是研究工业化、城市化和环境可持续性之间联系的重要资源,同时为政策制定者提供平衡经济和环境优先事项的见解。
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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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