Detection and automatic identification of loess sinkholes from the perspective of LiDAR point clouds and deep learning algorithm-Reference-Cited by-同舟云学术

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Detection and automatic identification of loess sinkholes from the perspective of LiDAR point clouds and deep learning algorithm

Published:2024-11 Issue: Volume:465 Page:109404
ISSN:0169-555X
Container-title:Geomorphology
language:en
Short-container-title:Geomorphology

Author:

Jiang Zongda,Hu Sheng,Deng Hao,Wang Ninglian,Zhang Fanyu,Wang Lin,Wu Songbai,Wang Xingang,Cao Zhengwen,Chen Yixian,Li Sisi

Funder

Northwest University

Ministry of Science and Technology of the People's Republic of China

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Elsevier BV

Reference53 articles.

1. Sinkholes, subsidence and subrosion on the eastern shore of the Dead Sea as revealed by a close-range photogrammetric survey;Al-Halbouni;Geomorphology,2017

2. Deep-Learning-based Automatic Sinkhole Recognition: Application to the Eastern Dead Sea;Alrabayah;Remote Sens.,2024

3. Analysis of dolines using multiple methods applied to airborne laser scanning data;Bauer;Geomorphology,2015

4. Can soil piping impact environment and society? Identifying new research gaps;Bernatek-Jakiel;Earth Surf. Process. Landf.,2023

5. Subsurface erosion by soil piping: significance and research needs;Bernatek-Jakiel;Earth Sci. Rev.,2018

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