Sen-2 LULC:土地使用土地覆盖数据集用于深度学习方法
Suraj Sawant1,2, Rahul Dev Garg1, Vishal Meshram3
1Geomatics Engineering, IIT Roorkee, Uttarakhand 247667, India.
Data in brief
|November 15, 2023
概括
包含213,761张高分辨率图像的Sen-2 LULC数据集推进了土地使用土地覆盖面 (LULC) 的分类. 该资源有助于研究人员了解环境动态和城市规划,特别是在印度.
科学领域:
- 遥感和地理空间分析
- 计算机视觉和机器学习
- 环境科学与城市规划
背景情况:
- 土地使用 土地覆盖 (LULC) 分类对于可持续的环境管理和自然资源规划至关重要.
- 传统的LULC绘图方法虽然有用,但在准确性和效率方面存在局限性.
- 深度学习与遥感数据的整合有可能在LULC分类中取得重大进展.
研究的目的:
- 介绍"Sen-2 LULC数据集",这是一个新的资源,旨在弥合计算机视觉和远程传感之间的差距,用于LULC分类.
- 提供全面的数据集,使先进的LULC映射和分析成为可能.
- 促进环境动力学,城市规划和生态系统变化的研究,特别是在印度地区.
主要方法:
- 开发"Sen-2 LULC数据集",使用来自科珀尼克斯开放访问中心的Sentinel-2卫星图像.
- 包括预处理的10米分辨率RGB图像和相应的面膜图像,代表七个不同的LULC类.
- 数据集结构为培训,测试和验证集,每个图像可能包含多个共存的土地覆盖类别,以反映现实世界的复杂性.
主要成果:
- 该数据集包括213,761张图像,为培训和评估LULC分类模型提供了丰富的资源.
- 每张图像中包含多个并存的类增加了数据集对复杂的现实世界景观的适用性.
- 数据集的光谱分辨率 (10m) 和RGB频道 (B4,B3,B2) 支持详细的土地覆盖面分析.
结论:
- "Sen-2 LULC数据集"是对遥感和计算机视觉领域的宝贵贡献,促进了先进的LULC分类.
- 预计这项资源将推动环境监测,城市规划和生态系统变化研究方面的创新.
- 该数据集促进了遥感和计算机视觉社区之间的协作,使得人们能够更深入地了解景观动态.
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