CBRA: 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
Abstract. 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 modelling to provide benefits to human well-being. However, it is still challenging to produce large-scale BRA due to the rather tiny size of individual buildings. From the viewpoint of classification methods, conventional approaches utilize high-resolution aerial images (metric or sub-metric 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 spatio-temporal scale. From the viewpoint of learning strategies, there is a non-trivial 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, hence there are no full-coverage maps of BRA in China yet. In this paper, we first propose a deep-learning method, named 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 dataset (CBRA) with 2.5 m resolution from 2016–2021 Sentinel-2 images. The CBRA is the first full-coverage and multi-annual BRA data in China. With the designed training sample generation algorithms and the spatio-temporal aware learning strategies, the CBRA achieves good performance with the F1 score of 62.55 % (+10.61 % compared with the previous BRA data in China) based on 250,000 testing samples in urban areas, and the recall of 78.94 % based on 30,000 testing samples in rural areas. Temporal analysis shows good performance consistency over years and the well agreement to other multi-annual impervious surface area datasets. The STSR-Seg will enable low-cost, dynamic and large-scale BRA mapping (https://github.com/zpl99/STSR-Seg). The CBRA will foster the development of BRA mapping and therefore provide basic data for sustainable research (Liu et al., 2023; https://doi.org/10.5281/zenodo.7500612).
Zeping Liu et al.
Status: open (until 06 Apr 2023)
- RC1: 'Comment on essd-2023-5', Anonymous Referee #1, 04 Mar 2023 reply
- RC2: 'Comment on essd-2023-5', Anonymous Referee #2, 17 Mar 2023 reply
- RC3: 'Comment on essd-2023-5', Anonymous Referee #3, 30 Mar 2023 reply
Zeping Liu et al.
CBRA: 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 https://doi.org/10.5281/zenodo.7500612
Model code and software
Spatio-Temporal aware Super-Resolution SEGmentation framework https://github.com/zpl99/STSR-Seg
Zeping Liu et al.
Viewed (geographical distribution)
The manuscript presents the China Building Rooftop Area (CBRA) dataset, which provides national-scale pixel-level information on individual building rooftop distribution and multi-annual dynamics from 2016 to 2021. The authors proposed an interesting and novel method for extracting high-resolution production of building rooftop from Sentinel images that could potentially reduce data acquisition costs. The study is well-structured, and the results demonstrate characteristics and superiority over previous production. The paper could be a valuable contribution to the society of urban remote sensing in terms of both the novel methodology and production. However, some revisions are necessary before accepting it for publication.