Uncertainty Reduction of Unlabeled Features in Landslide Inventory Using Machine Learning t-SNE Clustering and Data Mining Apriori Association Rule Algorithms

Uncertainty Reduction of Unlabeled Features in Landslide Inventory Using Machine Learning t-SNE Clustering and Data Mining Apriori Association Rule Algorithms

A landslide inventory, after an intense rainfall event in 1998, Southwestern Korea, was collected by digitizing aerial photographs. This left high uncertainty in the inventoried features to be verified by ground truths. To reduce the uncertainty, the photographs were reexamined, supported by the tim...

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Bibliographic Details
Main Authors: Omar F. Althuwaynee, Ali Aydda, In-Tak Hwang, Yoon-Kyung Lee, Sang-Wan Kim, Hyuck-Jin Park, Moon-Se Lee, Yura Park
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Applied Sciences
Subjects:
automatic change detection
machine learning
landslide inventory
uncertainty
distribution pattern
Online Access:https://www.mdpi.com/2076-3417/11/2/556

Internet

https://www.mdpi.com/2076-3417/11/2/556

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