Mo Shaoyi, Shi Yufeng, Yuan Qi, Li Mingyue
College of Civil Engineering, Nanjing Forestry University, Nanjing 210047, China.
School of Foreign Studies, Nanjing Forestry University, Nanjing 210047, China.
Sensors (Basel). 2024 Mar 6;24(5):1708. doi: 10.3390/s24051708.
Roads are the fundamental elements of transportation, connecting cities and rural areas, as well as people's lives and work. They play a significant role in various areas such as map updates, economic development, tourism, and disaster management. The automatic extraction of road features from high-resolution remote sensing images has always been a hot and challenging topic in the field of remote sensing, and deep learning network models are widely used to extract roads from remote sensing images in recent years. In light of this, this paper systematically reviews and summarizes the deep-learning-based techniques for automatic road extraction from high-resolution remote sensing images. It reviews the application of deep learning network models in road extraction tasks and classifies these models into fully supervised learning, semi-supervised learning, and weakly supervised learning based on their use of labels. Finally, a summary and outlook of the current development of deep learning techniques in road extraction are provided.
道路是交通的基本要素，连接着城市与乡村，以及人们的生活和工作。它们在地图更新、经济发展、旅游业和灾害管理等各个领域发挥着重要作用。从高分辨率遥感影像中自动提取道路特征一直是遥感领域的一个热门且具有挑战性的课题，近年来深度学习网络模型被广泛用于从遥感影像中提取道路。鉴于此，本文系统地回顾和总结了基于深度学习从高分辨率遥感影像中自动提取道路的技术。它回顾了深度学习网络模型在道路提取任务中的应用，并根据其对标签的使用情况将这些模型分为全监督学习、半监督学习和弱监督学习。最后，对道路提取中深度学习技术的当前发展进行了总结和展望。
