AI Research

    Towards Label-Efficient Geospatial Foundation Model Adaptation via Positive-Unlabeled Learning for Building Damage Mapping

    A label-efficient approach explores adapting geospatial foundation models for building-damage mapping

    Geospatial foundation models (GFMs) are reshaping remote sensing by shifting the field from task-specific modeling toward general-purpose geospatial intelligence.

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    ArXiv CS.CV

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    ArXiv CS.CV
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    Adapting geospatial foundation models with fewer labels could make building-damage mapping more practical where annotated data is limited. Positive-unlabeled learning offers a route the research explores for that adaptation.

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    #Positive Unlabeled Learning#Geospatial Ai#Building Damage

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