利用机器学习生成统一完整的德国建筑高度数据集

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-07-31 DOI:10.1016/j.egyai.2024.100408
Kristina Dabrock , Noah Pflugradt , Jann Michael Weinand , Detlef Stolten
{"title":"利用机器学习生成统一完整的德国建筑高度数据集","authors":"Kristina Dabrock ,&nbsp;Noah Pflugradt ,&nbsp;Jann Michael Weinand ,&nbsp;Detlef Stolten","doi":"10.1016/j.egyai.2024.100408","DOIUrl":null,"url":null,"abstract":"<div><p>Building geometry data is crucial for detailed, spatially-explicit analyses of the building stock in energy systems analysis and beyond. Despite the existence of diverse datasets and methods, a standardized and validated approach for creating a nation-wide unified and complete dataset of German building heights is not yet available. This study develops and validates such a methodology, combining different data sources for building footprints and heights and filling gaps in height data using an XGBoost machine learning algorithm. The XGBoost model achieves a mean absolute error of 1.78 m at the national level and between 1.52 m and 3.47 m at the federal state level. The goal is proving the applicability of the methodology at a large scale and creating a useful dataset. The resulting dataset is thoroughly evaluated on a building-by-building level and spatially resolved statistics on the quality of the dataset are reported. This detailed validation found that the building number and footprint area of German building stock is 90.31 % and 94.84 % correct, respectively, and the building height accuracy is 0.59 m at the national level. However, errors are not homogeneous across Germany and further research is needed into the impact of including additional datasets, especially for regions and building types with lower accuracies. This study proves that the chosen methodology is useful for generating a building height dataset and the workflow, with some modifications for regional data availability, can be transferred to other countries. The generated building dataset for Germany constitutes a valuable data basis for the research community in fields such as energy research, urban planning and building decarbonization policy development.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100408"},"PeriodicalIF":9.6000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000740/pdfft?md5=0c0b5b01fe19056c6830a6c702ac7eb8&pid=1-s2.0-S2666546824000740-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Leveraging machine learning to generate a unified and complete building height dataset for Germany\",\"authors\":\"Kristina Dabrock ,&nbsp;Noah Pflugradt ,&nbsp;Jann Michael Weinand ,&nbsp;Detlef Stolten\",\"doi\":\"10.1016/j.egyai.2024.100408\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Building geometry data is crucial for detailed, spatially-explicit analyses of the building stock in energy systems analysis and beyond. Despite the existence of diverse datasets and methods, a standardized and validated approach for creating a nation-wide unified and complete dataset of German building heights is not yet available. This study develops and validates such a methodology, combining different data sources for building footprints and heights and filling gaps in height data using an XGBoost machine learning algorithm. The XGBoost model achieves a mean absolute error of 1.78 m at the national level and between 1.52 m and 3.47 m at the federal state level. The goal is proving the applicability of the methodology at a large scale and creating a useful dataset. The resulting dataset is thoroughly evaluated on a building-by-building level and spatially resolved statistics on the quality of the dataset are reported. This detailed validation found that the building number and footprint area of German building stock is 90.31 % and 94.84 % correct, respectively, and the building height accuracy is 0.59 m at the national level. However, errors are not homogeneous across Germany and further research is needed into the impact of including additional datasets, especially for regions and building types with lower accuracies. This study proves that the chosen methodology is useful for generating a building height dataset and the workflow, with some modifications for regional data availability, can be transferred to other countries. The generated building dataset for Germany constitutes a valuable data basis for the research community in fields such as energy research, urban planning and building decarbonization policy development.</p></div>\",\"PeriodicalId\":34138,\"journal\":{\"name\":\"Energy and AI\",\"volume\":\"17 \",\"pages\":\"Article 100408\"},\"PeriodicalIF\":9.6000,\"publicationDate\":\"2024-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666546824000740/pdfft?md5=0c0b5b01fe19056c6830a6c702ac7eb8&pid=1-s2.0-S2666546824000740-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy and AI\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666546824000740\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy and AI","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666546824000740","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

建筑几何数据对于在能源系统分析及其他方面对建筑群进行详细的空间分析至关重要。尽管存在各种不同的数据集和方法,但目前还没有一种标准化的、经过验证的方法来创建一个全国统一的、完整的德国建筑高度数据集。本研究开发并验证了这种方法,它结合了建筑占地面积和高度的不同数据源,并使用 XGBoost 机器学习算法填补了高度数据的空白。XGBoost 模型在国家一级的平均绝对误差为 1.78 米,在联邦州一级的平均绝对误差为 1.52 米至 3.47 米。目标是证明该方法的大规模适用性,并创建一个有用的数据集。我们对生成的数据集进行了逐栋建筑的全面评估,并报告了数据集质量的空间分辨率统计数据。详细的验证结果表明,德国建筑群的建筑数量和占地面积的正确率分别为 90.31% 和 94.84%,全国范围内的建筑高度精确度为 0.59 米。然而,德国各地的误差并不一致,因此需要进一步研究加入额外数据集的影响,尤其是对精确度较低的地区和建筑类型的影响。这项研究证明,所选择的方法对于生成建筑高度数据集非常有用,而且工作流程在根据地区数据可用性进行一些修改后,也可以推广到其他国家。生成的德国建筑数据集为能源研究、城市规划和建筑脱碳政策制定等领域的研究人员提供了宝贵的数据基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Leveraging machine learning to generate a unified and complete building height dataset for Germany

Building geometry data is crucial for detailed, spatially-explicit analyses of the building stock in energy systems analysis and beyond. Despite the existence of diverse datasets and methods, a standardized and validated approach for creating a nation-wide unified and complete dataset of German building heights is not yet available. This study develops and validates such a methodology, combining different data sources for building footprints and heights and filling gaps in height data using an XGBoost machine learning algorithm. The XGBoost model achieves a mean absolute error of 1.78 m at the national level and between 1.52 m and 3.47 m at the federal state level. The goal is proving the applicability of the methodology at a large scale and creating a useful dataset. The resulting dataset is thoroughly evaluated on a building-by-building level and spatially resolved statistics on the quality of the dataset are reported. This detailed validation found that the building number and footprint area of German building stock is 90.31 % and 94.84 % correct, respectively, and the building height accuracy is 0.59 m at the national level. However, errors are not homogeneous across Germany and further research is needed into the impact of including additional datasets, especially for regions and building types with lower accuracies. This study proves that the chosen methodology is useful for generating a building height dataset and the workflow, with some modifications for regional data availability, can be transferred to other countries. The generated building dataset for Germany constitutes a valuable data basis for the research community in fields such as energy research, urban planning and building decarbonization policy development.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
期刊最新文献
Predicting the thermal conductivity of polymer composites with one-dimensional oriented fillers using the combination of deep learning and ensemble learning A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction Integrating local knowledge with ChatGPT-like large-scale language models for enhanced societal comprehension of carbon neutrality Optimization of a Bayesian game for Peer-to-Peer trading among prosumers under incomplete information via a CNN-LSTM-ATT Parameter sensitivity analysis for diesel spray penetration prediction based on GA-BP neural network
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1