摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-关于计算智能的最新研究结果已经发表。根据NewsRx记者在西甸大学的新闻报道，研究表明：“从高分辨率遥感图像中提取道路一直是计算机视觉领域的一个热点和挑战性的研究课题。”本研究的资金来源包括国家科技重点项目、国家自然科学基金、广西可信软件重点实验室、陕西省重点研究开发项目、中央高校基础研究基金。《新闻编辑》引用了西甸大学的一篇研究报告：“道路网络的Acc提取在城市规划、交通监测、灾害响应和环境监测等领域具有广泛的实用价值，随着计算智能领域的迅速发展，特别是深度学习技术的突破，道路网络的Acc提取在城市规划、交通监测、灾害响应和环境监测等领域具有广泛的应用价值。”道路提取技术已经取得了重大的进步和创新,本文系统地综述了基于深度学习的遥感图像道路提取方法,重点分析了计算智能技术在提高道路提取精度和效率方面的应用,并根据标注数据的类型,将基于深度学习的方法分为完全监督学习、半监督学习和基于深度学习的方法。无监督学习方法和无监督学习方法，每种方法都进一步分为更具体的子类别，并根据它们的原理、优点和局限性进行比较分析。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on computational intelligence have been published. According to news reporting from Xidian Unive rsity by NewsRx journalists, research stated, "Road extraction from high-resolut ion remote sensing images has long been a focal and challenging research topic i n the field of computer vision." Funders for this research include National Science And Technology Major Project; National Natural Science Foundation of China; Guangxi Key Laboratory of Trusted Software; Provincial Key Research And Development Program of Shaanxi; Fundament al Research Funds For The Central Universities. The news editors obtained a quote from the research from Xidian University: "Acc urate extraction of road networks holds extensive practical value in various fie lds, such as urban planning, traffic monitoring, disaster response and environme ntal monitoring. With rapid development in the field of computational intelligen ce, particularly breakthroughs in deep learning technology, road extraction tech nology has made significant progress and innovation. This paper provides a syste matic review of deep learning-based methods for road extraction from remote sens ing images, focusing on analyzing the application of computational intelligence technologies in improving the precision and efficiency of road extraction. Accor ding to the type of annotated data, deep learning-based methods are categorized into fully supervised learning, semi-supervised learning, and unsupervised learn ing approaches, each further divided into more specific subcategories. They are comparatively analyzed based on their principles, advantages, and limitations."
NETL
NSTL