China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery
<p>Large-scale and multi-annual maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In addition, as a fine-grained indicator of human activities, BRA could contribute to urban planning and energy modeling to provide benefits to human wel...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-08-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/15/3547/2023/essd-15-3547-2023.pdf |
| _version_ | 1797748947219906560 |
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| author | Z. Liu Z. Liu H. Tang H. Tang H. Tang L. Feng S. Lyu |
| author_facet | Z. Liu Z. Liu H. Tang H. Tang H. Tang L. Feng S. Lyu |
| author_sort | Z. Liu |
| collection | DOAJ |
| description | <p>Large-scale and multi-annual maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In
addition, as a fine-grained indicator of human activities, BRA could
contribute to urban planning and energy modeling to provide benefits to human well-being. However, it is still challenging to produce a large-scale BRA due to the rather tiny sizes of individual buildings. From the viewpoint
of classification methods, conventional approaches utilize high-resolution
aerial images (metric or submetric resolution) to map BRA; unfortunately,
high-resolution imagery is both infrequently captured and expensive to
purchase, making the BRA mapping costly and inadequate over a consistent
spatiotemporal scale. From the viewpoint of learning strategies, there is a
nontrivial gap that persists between the limited training references and
the applications over geospatial variations. Despite the difficulties,
existing large-scale BRA datasets, such as those from Microsoft or Google,
do not include China, and hence there are no full-coverage maps of BRA in China
yet. In this paper, we first propose a deep-learning method, named the
Spatio-Temporal aware Super-Resolution Segmentation framework (STSR-Seg), to
achieve robust super-resolution BRA extraction from relatively
low-resolution imagery over a large geographic space. Then, we produce the
multi-annual China Building Rooftop Area (CBRA) dataset with 2.5 m
resolution from 2016–2021 Sentinel-2 images. CBRA is the first
full-coverage and multi-annual BRA dataset in China. With the designed training-sample-generation algorithms and the spatiotemporally aware learning
strategies, CBRA achieves good performance with a F1 score of 62.55 % (<span class="inline-formula">+10.61</span> % compared with the previous BRA data in China) based
on 250 000 testing samples in urban areas and a recall of 78.94 % based
on 30 000 testing samples in rural areas. Temporal analysis shows good
performance consistency over years and good agreement with other
multi-annual impervious surface area datasets. STSR-Seg will enable low-cost, dynamic, and large-scale BRA mapping
(<span class="uri">https://github.com/zpl99/STSR-Seg</span>, last access: 12 July 2023). CBRA will foster the development of BRA mapping and therefore provide basic data for sustainable research (Liu
et al., 2023; <a href="https://doi.org/10.5281/zenodo.7500612">https://doi.org/10.5281/zenodo.7500612</a>).</p> |
| first_indexed | 2024-03-12T16:13:13Z |
| format | Article |
| id | doaj.art-3090396f4438402a877b487c2c703d41 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-03-12T16:13:13Z |
| publishDate | 2023-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-3090396f4438402a877b487c2c703d412023-08-09T10:55:29ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162023-08-01153547357210.5194/essd-15-3547-2023China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imageryZ. Liu0Z. Liu1H. Tang2H. Tang3H. Tang4L. Feng5S. Lyu6Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, ChinaBeijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaKey Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, ChinaBeijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaBeijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaBeijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China<p>Large-scale and multi-annual maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In addition, as a fine-grained indicator of human activities, BRA could contribute to urban planning and energy modeling to provide benefits to human well-being. However, it is still challenging to produce a large-scale BRA due to the rather tiny sizes of individual buildings. From the viewpoint of classification methods, conventional approaches utilize high-resolution aerial images (metric or submetric resolution) to map BRA; unfortunately, high-resolution imagery is both infrequently captured and expensive to purchase, making the BRA mapping costly and inadequate over a consistent spatiotemporal scale. From the viewpoint of learning strategies, there is a nontrivial gap that persists between the limited training references and the applications over geospatial variations. Despite the difficulties, existing large-scale BRA datasets, such as those from Microsoft or Google, do not include China, and hence there are no full-coverage maps of BRA in China yet. In this paper, we first propose a deep-learning method, named the Spatio-Temporal aware Super-Resolution Segmentation framework (STSR-Seg), to achieve robust super-resolution BRA extraction from relatively low-resolution imagery over a large geographic space. Then, we produce the multi-annual China Building Rooftop Area (CBRA) dataset with 2.5 m resolution from 2016–2021 Sentinel-2 images. CBRA is the first full-coverage and multi-annual BRA dataset in China. With the designed training-sample-generation algorithms and the spatiotemporally aware learning strategies, CBRA achieves good performance with a F1 score of 62.55 % (<span class="inline-formula">+10.61</span> % compared with the previous BRA data in China) based on 250 000 testing samples in urban areas and a recall of 78.94 % based on 30 000 testing samples in rural areas. Temporal analysis shows good performance consistency over years and good agreement with other multi-annual impervious surface area datasets. STSR-Seg will enable low-cost, dynamic, and large-scale BRA mapping (<span class="uri">https://github.com/zpl99/STSR-Seg</span>, last access: 12 July 2023). CBRA will foster the development of BRA mapping and therefore provide basic data for sustainable research (Liu et al., 2023; <a href="https://doi.org/10.5281/zenodo.7500612">https://doi.org/10.5281/zenodo.7500612</a>).</p>https://essd.copernicus.org/articles/15/3547/2023/essd-15-3547-2023.pdf |
| spellingShingle | Z. Liu Z. Liu H. Tang H. Tang H. Tang L. Feng S. Lyu China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery Earth System Science Data |
| title | China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery |
| title_full | China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery |
| title_fullStr | China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery |
| title_full_unstemmed | China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery |
| title_short | China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery |
| title_sort | china building rooftop area the first multi annual 2016 2021 and high resolution 2 5 thinsp m building rooftop area dataset in china derived with super resolution segmentation from sentinel 2 imagery |
| url | https://essd.copernicus.org/articles/15/3547/2023/essd-15-3547-2023.pdf |
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