对使用高分辨率遥感图像的深度学习道路提取算法的调查
Shaoyi Mo1, Yufeng Shi1, Qi Yuan1
1College of Civil Engineering, Nanjing Forestry University, Nanjing 210047, China.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
本综述探讨了从高分辨率遥感图像中自动提取道路的深度学习. 它根据标签的使用方式对方法进行分类,并提供了对道路绘图当前进展和未来方向的见解.
科学领域:
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
- 人工智能的人工智能
背景情况:
- 道路网络对于交通,经济发展和灾害管理至关重要.
- 从高分辨率遥感图像中自动提取道路是一项具有挑战性但至关重要的任务.
- 深度学习模型已成为分析遥感数据的重要工具.
研究的目的:
- 系统地审查和总结基于深度学习的自动道路开采技术.
- 根据其监督级别 (完全,半监督和弱监督) 来分类深度学习模型.
- 提供关于道路开采中的深度学习应用的全面概述和未来展望.
主要方法:
- 关于用于道路开采的深度学习的最新文献的综述.
- 基于数据标签策略的深度学习模型的分类.
- 对模型性能和在各种场景中的适用性进行分析.
主要成果:
- 深度学习模型显著提高了自动道路开采的准确性和效率.
- 分类揭示了监督,半监督和弱监督方法的明显优势和挑战.
- 综述综合了广泛的深度学习技术应用到这个领域.
结论:
- 深度学习是一种强大的方法,可以从遥感数据中自动提取道路.
- 需要进一步的研究,以优化各种条件的模型,并提高标签效率.
- 该领域正在迅速发展,未来在地理空间智能领域的应用具有显著的潜力.
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