基于Sentinel-2的数据集涵盖印度地区的道路检测:基础设施监测数据集
Avishkar Karnik1, Shilpa Pimpalkar2, Manjusree Panchagnula3
1Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India.
Data in brief
|November 10, 2025
概括
一个新的Sentinel-2道路数据集有助于从卫星图像中提取道路. 使用预处理技术增强图像显著改善了道路细分的深度学习模型性能.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 地理空间分析是什么
背景情况:
- 从卫星图像中提取道路对于基础设施监测,运输规划和自主导航至关重要.
- 卫星Sentinel-2的数据为公众提供了一种可供使用的替代方案,用于用于道路开采等遥感应用的非常高分辨率 (VHR) 图像.
研究的目的:
- 引入Sentinel-2道路数据集,这是使用Sentinel-2卫星图像进行道路细分的公开资源.
- 为评估和开发用于道路开采的深度学习模型提供一个基准数据集.
主要方法:
- 该数据集包括来自印度各个地区的Sentinel-2图像 (10米分辨率),使用来自OpenStreetMap (OSM) 数据的道路面具.
- 创建了包含马校正和CLAHE的增强图像版本,以改善道路可见性.
- 总共有5634个高质量的样本 (256x256像素) 被手工精制并以TIF和PNG格式提供.
主要成果:
- 在增强的Sentinel-2图像上训练的深度学习细分模型显示,与在原始图像上训练的模型相比,性能显著提高.
- 像马校正和CLAHE这样的预处理技术对道路分段精度产生了积极的影响.
结论:
- 卫星-2公路数据集是计算机视觉和遥感研究人员的宝贵资源.
- 该数据集促进了利用卫星图像的先进道路细分模型的进一步研究和开发.
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