School of Geospatial Engineering and Science, Sun Yat-Sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China.
International Research Center of Big Data for Sustainable Development Goals, Beijing, China.
Sci Data. 2024 Feb 10;11(1):187. doi: 10.1038/s41597-024-03009-5.
Accurate building extraction is crucial for urban understanding, but it often requires a substantial number of building samples. While some building datasets are available for model training, there remains a lack of high-quality building datasets covering urban and rural areas in China. To fill this gap, this study creates a high-resolution GaoFen-7 (GF-7) Building dataset utilizing the Chinese GF-7 imagery from six Chinese cities. The dataset comprises 5,175 pairs of 512 × 512 image tiles, covering 573.17 km. It contains 170,015 buildings, with 84.8% of the buildings in urban areas and 15.2% in rural areas. The usability of the GF-7 Building dataset has been proved with seven convolutional neural networks, all achieving an overall accuracy (OA) exceeding 93%. Experiments have shown that the GF-7 building dataset can be used for building extraction in urban and rural scenarios. The proposed dataset boasts high quality and high diversity. It supplements existing building datasets and will contribute to promoting new algorithms for building extraction, as well as facilitating intelligent building interpretation in China.
精确的建筑物提取对于城市理解至关重要，但通常需要大量的建筑物样本。虽然有一些建筑物数据集可用于模型训练，但中国城乡地区仍缺乏高质量的建筑物数据集。为了填补这一空白，本研究利用中国 GF-7 卫星的六座中国城市的影像，创建了一个高分辨率的 GF-7 建筑物数据集。该数据集包含 5175 对 512×512 图像瓦片，覆盖 573.17km²。它包含 170015 栋建筑物，其中 84.8%位于城市地区，15.2%位于农村地区。七个卷积神经网络已经证明了 GF-7 建筑物数据集的可用性，所有网络的总体精度（OA）都超过 93%。实验表明，GF-7 建筑物数据集可用于城市和农村场景的建筑物提取。所提出的数据集具有高质量和多样性。它补充了现有的建筑物数据集，将有助于推动建筑物提取的新算法，并促进中国的智能建筑物解释。
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