An Integrated Framework of Positive-Unlabeled and Imbalanced Learning for Landslide Susceptibility Mapping

Machine learning is pivotal in data-driven landslide susceptibility mapping (LSM). However, the uncertainty of negative samples and the imbalance between positive and negative samples, which leads to misjudgments and overestimation, remain ongoing challenges. This study introduces a novel framework...

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Detalles Bibliográficos

Autores principales:	Zijin Fu, Hao Ma, Fawu Wang, Jie Dou, Bo Zhang, Zhice Fang
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	IEEE 2024-01-01
Colección:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Materias:	Data-driven landslide susceptibility mapping (LSM) imbalanced learning negative samples positive-unlabeled (PU) learning
Acceso en línea:	https://ieeexplore.ieee.org/document/10660469/
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