Weakly Supervised Learning For Geospatial Data

Weakly Supervised Learning for Geospatial Data is a machine learning paradigm that leverages incomplete, imprecise, or noisy labels—such as image-level annotations, bounding boxes, or proxy variables—to train models on spatially referenced data, thereby reducing the reliance on densely annotated ground truth while accounting for spatial autocorrelation, scale, and heterogeneity inherent in geospatial applications.

16

Publications

1.3K

Citations

74

Authors

39

Institutions

Publications per year

2017–2026

12

Authors

74

  1. XJ
    Xiuping Jia

    UNSW Sydney, Australia

    2 publications · 49 citations

    2

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  2. JH
    Junwei Han

    Northwestern Polytechnical University, China

    2 publications · 759 citations

    2

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  3. MS
    Michael Schmitt

    Technical University of Munich, Germany

    2 publications · 85 citations

    2

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  4. LG
    Lei Guo

    Northwestern Polytechnical University, China

    2 publications · 759 citations

    2

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  5. GC
    Gong Cheng

    Northwestern Polytechnical University, China

    2 publications · 759 citations

    2

    h-index

1–5 of 74

Venues

  1. 2

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  2. 2

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  3. Remote Sensing

    2 publications · 43 citations

    2

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  4. 2

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  5. IEEE Access

    1 publications · 57 citations

    1

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1–5