基于对比损失的新型滑坡易发性预测框架

IF 6 2区 地球科学 Q1 GEOGRAPHY, PHYSICAL GIScience & Remote Sensing Pub Date : 2024-02-02 DOI:10.1080/15481603.2024.2306740
Shubing Ouyang, Weitao Chen, Hangyuan Liu, Yuanyao Li, Zhanya Xu
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引用次数: 0

摘要

最近，事实证明正向无标记（PU）学习算法在生成准确的滑坡易感性地图方面非常有效。这些算法将样本完全分为正向和非正向两类。
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A novel landslide susceptibility prediction framework based on contrastive loss
Recently, the positive unlabeled (PU) learning algorithms have proven highly effective in generating accurate landslide susceptibility maps. The algorithms categorize samples exclusively into posit...
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来源期刊
GIScience & Remote Sensing
GIScience & Remote Sensing 地学-遥感
CiteScore
11.20
自引率
9.00%
发文量
84
审稿时长
6 months
期刊介绍： GIScience & Remote Sensing publishes original, peer-reviewed articles associated with geographic information systems (GIS), remote sensing of the environment (including digital image processing), geocomputation, spatial data mining, and geographic environmental modelling. Papers reflecting both basic and applied research are published.
期刊最新文献
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