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    • Multitask learning for unsupervised domain adaptive semantic segmentation of remote sensing images

    • “Remote sensing image semantic segmentation plays an important role in land cover and use classification, urban planning, and change detection. As a highly promising unsupervised learning method, domain adaptation technology has greatly promoted the development of semantic segmentation in remote sensing images. However, existing models are still based on single task learning, and the segmentation features obtained from learning are not sufficient, resulting in difficulty in accurately identifying complex regions in remote sensing images during the segmentation process. To address this issue, experts have proposed a multi task learning domain adaptive semantic segmentation network MTLDANet, which enhances the learning ability of segmentation features by synergistically learning semantic and elevation information in remote sensing images.”
      • role：First author第一作者
      • Affiliation：

        School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430078, China

      • Email：1202321619@cug.edu.cn
      • Introduction：王渝，研究方向为遥感图像的语义分割。E-mail：1202321619@cug.edu.cn

      WANG Yu

      1,
      • Affiliation：

        School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430078, China

      FENG Yuting

      1,
      • Affiliation：

        School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430078, China

      GONG Sishi

      1,
      • Affiliation：

        School of Surveying, Mapping and Information Engineering, West Yunnan University of Applied Sciences, Dali 671006, China

      MAO Yanqin

      2,
      • Affiliation：

        School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430078, China

        Hubei Provincial Key Laboratory of Intelligent Geological Information Processing, Wuhan 430074, China

      LI Shengwen

      13,
      • Affiliation：

        School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430078, China

        Hubei Provincial Key Laboratory of Intelligent Geological Information Processing, Wuhan 430074, China

      FANG Fang

      13,
      • role：Corresponding author通信作者
      • Affiliation：

        School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430078, China

        Hubei Provincial Key Laboratory of Intelligent Geological Information Processing, Wuhan 430074, China

      • Email：zhoushunping@mapgis.com
      • Introduction：周顺平，研究方向为空间数据库技术、地理空间人工智能和计算机视觉。E-mail： zhoushunping@mapgis.com

      ZHOU Shunping

      13 *
    • Vol. 30, Issue 2, Pages: 325-346(2026)  

    • DOI：10.11834/jrs.20254411    

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