the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
3D-GloBFP: the first global three-dimensional building footprint dataset
Abstract. Understanding urban vertical structures, particularly building heights, is essential for examining the intricate interaction between humans and their environment. Such datasets are indispensable for a variety of applications, including climate modeling, energy consumption analysis, and socioeconomic activities. Despite the importance of this information, previous studies have primarily focused on estimating building heights regionally on a grid scale, often resulting in datasets with limited coverage or spatial resolution. This limitation hampers comprehensive global analyses and the ability to generate actionable insights on finer scales. In this study, we developed a global building height map (3D-GloBFP) at a building footprint scale by leveraging Earth Observation (EO) datasets and advanced machine learning techniques. Our approach integrated multisource remote sensing features and building morphology features to develop height estimation models using the eXtreme Gradient Boosting (XGBoost) regression method across diverse global regions. This methodology allowed us to estimate the heights of individual buildings worldwide, culminating in the creation of the first global three-dimensional (3-D) building footprints (3D-GloBFP). Our evaluation results show that the height estimation models perform exceptionally well on a worldwide scale, with R2 ranging from 0.66 to 0.96 and root mean square errors (RMSEs) ranging from 1.9 m to 14.6 m across 33 subregions. Comparisons with other datasets demonstrate that our 3D-GloBFP closely matches the distribution and spatial pattern of reference heights. Our derived 3-D global building footprint map shows a distinct spatial pattern of building heights across regions, countries, and cities, with building heights gradually decreasing from the city center to the surrounding rural areas. Furthermore, our findings indicate the disparities in built-up infrastructure (i.e., building volume) across different countries and cities. China is the country with the most intensive total built-up infrastructure (5.28×1011 m3, accounting for 23.9 % of the global total), followed by the United States (3.90×1011 m3, accounting for 17.6 % of the global total). Shanghai has the largest volume of built-up infrastructure (2.1×1010 m3) of all representative cities. The derived building-footprint scale height map (3D-GloBFP) reveals the significant heterogeneity of urban built-up environments, providing valuable insights for studies in urban socioeconomic dynamics and climatology. The 3D-GloBFP dataset is available at https://doi.org/10.5281/zenodo.11319913 (Building height of the Americas, Africa, and Oceania in 3D-GloBFP) (Che et al., 2024a), https://doi.org/10.5281/zenodo.11397015 (Building height of Asia in 3D-GloBFP) (Che et al., 2024b), and https://doi.org/10.5281/zenodo.11391077 (Building height of Europe in 3D-GloBFP) (Che et al., 2024c).
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RC1: 'Comment on essd-2024-217', Anonymous Referee #1, 28 Jun 2024
General comments
This article uses a practical method based on multimodal data to construct the first global scale 3D information dataset of buildings. The data set provided by this study fills the gap in fine-grained building height data globally, which is of great significance for urban morphology research and climate change analysis. The model validation results are comprehensive and promising; however, a more detailed explanation of the technical methods would enhance the paper's clarity (see specific comments). Overall, the paper is well-structured, and the dataset is valuable to urban studies.
Specific comments
- The article uses multiple sources of data for analysis, and it is recommended to add ablation experiments between different data to demonstrate the effectiveness of using the data.
- Why did you choose to use XGboost instead of random forest or support vector machine? Please provide additional experiments or explanations.
- Please explain the specific operation of manual measurement in section 4.2 and the basis for the authenticity of manual height measurement values.
- What are the advantages of building scale 3D data over coursescale 3D data set?
- Please explain in detail how to aggregate building scale height data to coarse resolution scalesfor validation.
- What are the contributions of statistical values in the model?
Citation: https://doi.org/10.5194/essd-2024-217-RC1 - AC1: 'Reply on RC1', Yangzi Che, 07 Sep 2024
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RC2: 'Comment on essd-2024-217', Anonymous Referee #2, 27 Jul 2024
This paper presents the world's first three-dimensional building footprint dataset, 3D-GloBFP, which integrates multi-source remote sensing data and various reference building height data. By employing machine learning methods, it generates high-precision global building height data. This dataset holds significant importance across multiple application domains, including urban planning, environmental monitoring, disaster management, and energy consumption analysis. The research demonstrates substantial promise and value, providing a crucial foundation for the acquisition and application of global 3D building data. I believe that 3D-GloBFP is an indispensable foundational dataset for urban research. During the review process, I identified several areas that require further clarification and improvement.
- In Section 3.2.1 "Division of Subregions," the information of training and testing samples (e.g., the total amount) for each sub-region should be explicitly provided. Clearly specifying the selection criteria and distribution of these samples will help readers better understand the process of model training and validation.
- I noticed that there are some missing tiles in Ghana and incomplete regions in Guangdong, Please ensure that the dataset is complete globally.
- In China, reference building heights used as training samples were mostly concentrated in city centers. Please clarify the accuracy of the model's estimates for building heights in different urban areas (e.g., urban fringe). This is crucial for validating the model's applicability in diverse environments.
- For high-rise buildings, especially super tall buildings, what are the potential reasons for height underestimation? It is recommended to include a detailed error analysis and explanation.
Citation: https://doi.org/10.5194/essd-2024-217-RC2 - AC2: 'Reply on RC2', Yangzi Che, 07 Sep 2024
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RC3: 'Comment on essd-2024-217', Anonymous Referee #3, 27 Jul 2024
The study by Che et al. represents a significant advancement in the field of urban geography and Earth observations. The development of the 3D-GloBFP dataset is a groundbreaking achievement that fills a critical gap in the availability of global, high-resolution, and accurate building height information. The methodology employed is innovative and rigorous, resulting in a dataset with exceptional performance and reliability. The implications and applications of the 3D-GloBFP dataset are vast, spanning from climate modeling to sustainable development policies. Overall, this study deserves high praise for its contributions to the scientific community and beyond. However, to further strengthen the research, I would suggest addressing the following minor issues:
The division of the 33 regions mentioned in the paper is not particularly clear and requires a brief elaboration or a reference to the specific figure where they are illustrated.
The first sentence of several paragraphs in the result section introduces the methodology, but it is recommended to revise them to summarize the findings of the current paragraph instead. You may place the corresponding methodology in the method section, or write it after the figure caption.
While most of the results presented in this paper appropriately utilize the present simple tense, there are some instances where the past tense has been used inappropriately, see for example, line 149.
Expanding the discussion of challenges and future work would provide valuable insights into the dataset's limitations and potential for growth. Identifying gaps in current knowledge, discussing opportunities for integrating additional data sources, and outlining plans for updating and maintaining the dataset over time would demonstrate the authors' commitment to ongoing improvement and research.
By addressing these points, the authors can enhance the readability and presentation of their work, thereby ensuring that the 3D-GloBFP dataset becomes an invaluable asset to the scientific community.
Citation: https://doi.org/10.5194/essd-2024-217-RC3 - AC3: 'Reply on RC3', Yangzi Che, 07 Sep 2024
Status: closed
-
RC1: 'Comment on essd-2024-217', Anonymous Referee #1, 28 Jun 2024
General comments
This article uses a practical method based on multimodal data to construct the first global scale 3D information dataset of buildings. The data set provided by this study fills the gap in fine-grained building height data globally, which is of great significance for urban morphology research and climate change analysis. The model validation results are comprehensive and promising; however, a more detailed explanation of the technical methods would enhance the paper's clarity (see specific comments). Overall, the paper is well-structured, and the dataset is valuable to urban studies.
Specific comments
- The article uses multiple sources of data for analysis, and it is recommended to add ablation experiments between different data to demonstrate the effectiveness of using the data.
- Why did you choose to use XGboost instead of random forest or support vector machine? Please provide additional experiments or explanations.
- Please explain the specific operation of manual measurement in section 4.2 and the basis for the authenticity of manual height measurement values.
- What are the advantages of building scale 3D data over coursescale 3D data set?
- Please explain in detail how to aggregate building scale height data to coarse resolution scalesfor validation.
- What are the contributions of statistical values in the model?
Citation: https://doi.org/10.5194/essd-2024-217-RC1 - AC1: 'Reply on RC1', Yangzi Che, 07 Sep 2024
-
RC2: 'Comment on essd-2024-217', Anonymous Referee #2, 27 Jul 2024
This paper presents the world's first three-dimensional building footprint dataset, 3D-GloBFP, which integrates multi-source remote sensing data and various reference building height data. By employing machine learning methods, it generates high-precision global building height data. This dataset holds significant importance across multiple application domains, including urban planning, environmental monitoring, disaster management, and energy consumption analysis. The research demonstrates substantial promise and value, providing a crucial foundation for the acquisition and application of global 3D building data. I believe that 3D-GloBFP is an indispensable foundational dataset for urban research. During the review process, I identified several areas that require further clarification and improvement.
- In Section 3.2.1 "Division of Subregions," the information of training and testing samples (e.g., the total amount) for each sub-region should be explicitly provided. Clearly specifying the selection criteria and distribution of these samples will help readers better understand the process of model training and validation.
- I noticed that there are some missing tiles in Ghana and incomplete regions in Guangdong, Please ensure that the dataset is complete globally.
- In China, reference building heights used as training samples were mostly concentrated in city centers. Please clarify the accuracy of the model's estimates for building heights in different urban areas (e.g., urban fringe). This is crucial for validating the model's applicability in diverse environments.
- For high-rise buildings, especially super tall buildings, what are the potential reasons for height underestimation? It is recommended to include a detailed error analysis and explanation.
Citation: https://doi.org/10.5194/essd-2024-217-RC2 - AC2: 'Reply on RC2', Yangzi Che, 07 Sep 2024
-
RC3: 'Comment on essd-2024-217', Anonymous Referee #3, 27 Jul 2024
The study by Che et al. represents a significant advancement in the field of urban geography and Earth observations. The development of the 3D-GloBFP dataset is a groundbreaking achievement that fills a critical gap in the availability of global, high-resolution, and accurate building height information. The methodology employed is innovative and rigorous, resulting in a dataset with exceptional performance and reliability. The implications and applications of the 3D-GloBFP dataset are vast, spanning from climate modeling to sustainable development policies. Overall, this study deserves high praise for its contributions to the scientific community and beyond. However, to further strengthen the research, I would suggest addressing the following minor issues:
The division of the 33 regions mentioned in the paper is not particularly clear and requires a brief elaboration or a reference to the specific figure where they are illustrated.
The first sentence of several paragraphs in the result section introduces the methodology, but it is recommended to revise them to summarize the findings of the current paragraph instead. You may place the corresponding methodology in the method section, or write it after the figure caption.
While most of the results presented in this paper appropriately utilize the present simple tense, there are some instances where the past tense has been used inappropriately, see for example, line 149.
Expanding the discussion of challenges and future work would provide valuable insights into the dataset's limitations and potential for growth. Identifying gaps in current knowledge, discussing opportunities for integrating additional data sources, and outlining plans for updating and maintaining the dataset over time would demonstrate the authors' commitment to ongoing improvement and research.
By addressing these points, the authors can enhance the readability and presentation of their work, thereby ensuring that the 3D-GloBFP dataset becomes an invaluable asset to the scientific community.
Citation: https://doi.org/10.5194/essd-2024-217-RC3 - AC3: 'Reply on RC3', Yangzi Che, 07 Sep 2024
Data sets
Building height of the Americas, Africa, and Oceania in 3D-GloBFP Yangzi Che et al. https://doi.org/10.5281/zenodo.11319913
Building height of Asia in 3D-GloBFP Yangzi Che et al. https://doi.org/10.5281/zenodo.11397015
Building height of Europe in 3D-GloBFP Yangzi Che et al. https://doi.org/10.5281/zenodo.11391077
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