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A novel landslide susceptibility prediction framework based on contrastive loss
GIScience & Remote Sensing ( IF 6.7 ) Pub Date : 2024-02-02 , DOI: 10.1080/15481603.2024.2306740 Shubing Ouyang 1 , Weitao Chen 1 , Hangyuan Liu 2 , Yuanyao Li 3 , Zhanya Xu 4
GIScience & Remote Sensing ( IF 6.7 ) Pub Date : 2024-02-02 , DOI: 10.1080/15481603.2024.2306740 Shubing Ouyang 1 , Weitao Chen 1 , Hangyuan Liu 2 , Yuanyao Li 3 , Zhanya Xu 4
Affiliation
Recently, the positive unlabeled (PU) learning algorithms have proven highly effective in generating accurate landslide susceptibility maps. The algorithms categorize samples exclusively into posit...
中文翻译：
一种基于对比损失的新型滑坡敏感性预测框架
最近，事实证明，正向无标记（PU）学习算法在生成准确的滑坡敏感性图方面非常有效。该算法将样本专门分类为位置...
更新日期：2024-02-07
中文翻译：
一种基于对比损失的新型滑坡敏感性预测框架
最近，事实证明，正向无标记（PU）学习算法在生成准确的滑坡敏感性图方面非常有效。该算法将样本专门分类为位置...
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