屋顶区域的全球高分辨率增长预测数据集与共享的社会经济道路相一致,2020-2050年
Siddharth Joshi1,2,3,4, Behnam Zakeri5,6, Shivika Mittal7,8
1SFI MaREI Centre for Energy Climate and Marine, Cork, Ireland. joshis@iiasa.ac.at.
Scientific data
|May 30, 2024
概括
这项研究估计了未来的全球屋顶面积增长,利用建筑数据的机器学习. 调查结果有助于可持续能源,城市规划和气候行动,支持可持续发展目标.
科学领域:
- 环境科学环境科学
- 城市规划是城市规划.
- 数据科学是数据科学.
背景情况:
- 估计全球屋顶面积对于去中心化能源系统,城市规划和实现可持续发展目标 (SDGs) 7,11,13和15至关重要.
- 准确的屋顶面积数据支持气候行动,能源转型,生物多样性研究和灾害风险管理.
研究的目的:
- 开发一个机器学习框架,用于全球协调的屋顶面积增长估计.
- 为基于共享社会经济路径 (SSP) 的五个未来增长场景提供数据.
- 支持气候变化,能源转型,城市规划和灾害风险管理方面的研究.
主要方法:
- 使用一个机器学习框架,在大约7亿个开源建筑足迹的大数据集上进行训练.
- 全球综合土地覆盖,道路和人口数据集,用于全面分析.
- 通过1/8度的空间分辨率在约350万个鱼网上生成估计.
主要成果:
- 从2020年到2050年生产全球协调的屋顶总面积增长估计.
- 为五个不同的未来增长叙述 (SSP) 提供了十年估计.
- 该数据集提供了大陆到城市级别的地理空间洞察力.
结论:
- 开发的数据集为各种科学应用提供了宝贵的资源,包括气候变化和城市发展.
- 促进可持续能源系统和弹性城市规划的知情决策.
- 通过数据驱动的洞察力支持全球可持续发展目标的推进.
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