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...
I tiakina i:
| Ngā kaituhi matua: | , , , , , |
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| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
IEEE
2024-01-01
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| Rangatū: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Ngā marau: | |
| Urunga tuihono: | https://ieeexplore.ieee.org/document/10660469/ |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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