测绘通报 ›› 2025, Vol. 0 ›› Issue (8): 174-178.doi: 10.13474/j.cnki.11-2246.2025.0829

• 测绘地理信息技术应用案例 • 上一篇    下一篇

城市立体感知网建设关键技术与监测应用——以广州市历史文化街区为例

胡耀锋1,2,3, 程相兵1,2,3, 陈嘉琦1,2,3   

  1. 1. 广州市城市规划勘测设计研究院有限公司, 广东 广州 510060;
    2. 广州市资源规划和海洋科技协同创新中心, 广东 广州 510060;
    3. 广东省城市感知与监测预警企业重点实验室, 广东 广州 510060
  • 收稿日期:2025-05-06 出版日期:2025-08-25 发布日期:2025-09-02
  • 作者简介:胡耀锋(1979—),男,教授级高级工程师,主要从事遥感科学应用研究工作。E-mail:40069588@qq.com
  • 基金资助:
    国家重点研发计划(2022YFC3803500)

Urban multi-dimensional sensing infrastructure: architectural innovations and monitoring applications

HU Yaofeng1,2,3, CHENG Xiangbing1,2,3, CHEN Jiaqi1,2,3   

  1. 1. Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd., Guangzhou 510060, China;
    2. Collaborative Innovation Center for Natural Resources Planning and Marine Technology of Guangzhou, Guangzhou 510060, China;
    3. Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China
  • Received:2025-05-06 Online:2025-08-25 Published:2025-09-02

摘要: 历史文化街区是城市历史文化遗产集中保存的核心地段,是历史文化名城保护的关键环节和重要内容。本文针对我国历史文化街区监督管理滞后、传统风貌屡遭破坏、动态监测手段落后等问题,提出了建设城市立体感知网的设想,研究了关键技术,并以广州市历史文化街区为试点,验证了感知网建设的显著成效。

关键词: 城市立体感知网, 无人机自动机场, 人工智能, 深度学习, 目标检测

Abstract: Historical and cultural blocks are core areas where urban historical and cultural heritage is centrally preserved,serving as a critical component and essential focus in the protection of historical and cultural cities.This paper addresses persistent issues in China's historical and cultural blocks,including lagging supervision and management,recurring damage to traditional architectural features,and outdated dynamic monitoring methods.We propose the concept of establishing an urban multi-dimensional sensing network,investigate its key technologies,and validate the significant effectiveness of the sensing network implementation through a pilot application in Guangzhou's historical and cultural blocks.

Key words: urban multi-dimensional sensing network, automated UAV base station, artificial intelligence, deep learning, object detection

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