Comment on essd-2023-5

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> 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&ndash;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 (<a href="https://github.com/zpl99/STSR-Seg" target="_blank" rel="noopener">https://github.com/zpl99/STSR-Seg</a>). The 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" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7500612</a>).