一个基准的GaoFen-7数据集用于从卫星图像中提取建筑物
Peimin Chen1, Huabing Huang2,3,4,5, Feng Ye1
1School of Geospatial Engineering and Science, Sun Yat-Sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China.
Scientific data
|February 10, 2024
概括
本研究介绍了来自中国的新型高分辨率建筑数据集.
科学领域:
- 遥感 遥感 遥感 遥感
- 地理空间数据科学数据科学
- 计算机视觉 计算机视觉
背景情况:
- 精确的建筑提取对于城市规划和分析至关重要.
- 现有的建筑数据集缺乏对中国多样化的城市和农村景观的全面覆盖.
- 高分辨率卫星图像对于详细的地理空间特征提取至关重要.
研究的目的:
- 为中国开发高质量,高分辨率的建筑数据集.
- 解决建筑数据的稀缺问题,涵盖中国城市和农村环境.
- 促进在构建提取算法和智能解释方面的进步.
主要方法:
- 使用GaoFen-7 (GF-7) 卫星图像创建一个新的建筑数据集.
- 数据集包括5,175个图像 (512x512像素),覆盖573.17平方公里.
- 包括170,015个建筑物,重点关注不同的城市 (84.8%) 和农村 (15.2%) 地区.
主要成果:
- GF-7建筑数据集显示出高质量和多样性.
- 卷积神经网络使用数据集实现了超过93%的整体准确性.
- 该数据集在城市和农村环境中对建筑物开采有效.
结论:
- GF-7建筑数据集有效地补充了现有的资源.
- 它支持开发先进的建筑提取技术.
- 该数据集将有助于在中国各地对智能建筑进行解释.
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