曹银霞，女，中国科学院地理科学与资源研究所，地理信息科学与技术全国重点实验室 助理研究员

研究领域与研究方向：

高分辨率遥感卫星智能解译、遥感大模型设计与应用、城市三维重建

教育背景：（倒序排列）

2018.09-2023.06 武汉大学 遥感信息工程学院 博士

2014.09-2018.06 武汉大学 测绘学院 学士

工作经历：（倒序排列）

2025.02~至今，中国科学院地理科学与资源研究所，地理信息科学与技术全国重点实验室 助理研究员

2023.07-2025.01，香港理工大学，博士后

科研业绩：

1.Cao Y,Huang X,Weng Q. A SAM-adapted weakly-supervised semantic segmentation method constrained by uncertainty and transformation consistency[J]. International Journal of Applied Earth Observation and Geoinformation,2025,137: 104440.

2.Cao Y,Weng Q. A deep learning-based super-resolution method for building height estimation at 2.5 m spatial resolution in the Northern Hemisphere[J]. Remote Sensing of Environment,2024,310: 114241.

3.Cao Y,Huang X,Weng Q. A multi-scale weakly supervised learning method with adaptive online noise correction for high-resolution change detection of built-up areas[J]. Remote Sensing of Environment,2023,297: 113779.

4.Cao Y,Huang X. A full-level fused cross-task transfer learning method for building change detection using noise-robust pretrained networks on crowdsourced labels[J]. Remote Sensing of Environment,2023,284: 113371.

5.Cao Y,Huang X. A coarse-to-fine weakly supervised learning method for green plastic cover segmentation using high-resolution remote sensing images[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2022,188: 157-176.

6.Cao Y,Huang X. A deep learning method for building height estimation using high-resolution multi-view imagery over urban areas: A case study of 42 Chinese cities[J]. Remote Sensing of Environment,2021,264: 112590.

7.Huang X1,Cao Y1,Li J. An automatic change detection method for monitoring newly constructed building areas using time-series multi-view high-resolution optical satellite images[J]. Remote Sensing of Environment,2020,244: 111802.

科研项目:

1. 国家自然科学基金青年科学基金项目，主持

2. 中国科学院战略性先导科技专项（B类），参与

联系方式：

通讯地址：北京市朝阳区大屯路甲11号 中国科学院地理科学与资源研究所

邮    编：100101

传    真：010-64889630

E-mail地址:caoyx@lreis.ac.cn