Spacecraft Recovery & Remote Sensing, Volume. 46, Issue 4, 59(2025)

The Standardized Multi-Modal Sample Dataset Organization Method and Application for Artificial Intelligence Interpretation

Junqi ZHAO1,2, Xiaofei MI2,3、*, Jian YANG2,3, Tao YU2,3, Xiaomin TIAN4, Chuanzhao TIAN4, Hongbo ZHU2,3, and Chuan LIU5,6
Author Affiliations
  • 1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
  • 2Aerospace Information Researchlnstitute, Chinese Academy of Sciences, Beijing 100094, China
  • 3Demonstration Center for Spaceborne Remote Sensing, China National Space Administration, Beijing 100101, China
  • 4School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China
  • 5Langfang Research and Development Center for Spatial information Technology, Langfang 065099, China
  • 6Zhongke Xingtong (Langfang) Information Technology Co., Ltd., Langfang 065000, China
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    With the rapid development of artificial intelligence technologies such as deep learning and machine learning, a large number of datasets have been generated in the field of remote sensing to meet the diverse needs of remote sensing tasks. However, these datasets exhibit significant differences in type, size, spatial and temporal resolution. To address this issue, this paper proposes a standardized multimodal sample dataset organization method oriented towards artificial intelligence interpretation. This establishes an effective link between samples and image data, forming a temporal sample dataset based on geographical spatial locations. This meets the demands of intelligent interpretation for a data cube that is scalable in terms of big data, multimodality, and samples. It achieves orderly management of massive remote sensing sample data while also supporting continuous expansion and reuse of samples. This paper applies the multimodal sample dataset organization method from both dataset and systematic management, providing effective data and technical support for artificial intelligence interpretation in various remote sensing tasks.

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    Junqi ZHAO, Xiaofei MI, Jian YANG, Tao YU, Xiaomin TIAN, Chuanzhao TIAN, Hongbo ZHU, Chuan LIU. The Standardized Multi-Modal Sample Dataset Organization Method and Application for Artificial Intelligence Interpretation[J]. Spacecraft Recovery & Remote Sensing, 2025, 46(4): 59

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    Paper Information

    Category: Remote Sensing Information Processing Technology

    Received: Jan. 26, 2025

    Accepted: Jan. 26, 2025

    Published Online: Sep. 12, 2025

    The Author Email: Xiaofei MI (mixf@aircas.ac.cn)

    DOI:10.3969/j.issn.1009-8518.2025.04.006

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