Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Yonggang Ge"'
Autor:
Yu Huang, Jianqiang Zhang, Haiqing He, Yang Jia, Rong Chen, Yonggang Ge, Zaiyang Ming, Lili Zhang, Haoyu Li
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 2586-2595 (2024)
Earthquake-triggered landslides (ETLs) are characterized by their extensive occurrences, having wide distributions. The conventional human–computer interaction extraction method is often time-consuming and labor-intensive, failing to meet the deman
Externí odkaz:
https://doaj.org/article/5a7d89d62e094262980361e59d99640f
Publikováno v:
ACS Omega, Vol 9, Iss 4, Pp 4775-4791 (2024)
Externí odkaz:
https://doaj.org/article/799aff2b40624e49a6dee0b8e7023bf5
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 14, Iss 1 (2023)
AbstractDebris flow occurs frequently in mountainous areas due to the special geographical and geological environment, causing significant damage to linear infrastructure. However, a systematic assessment of debris flow risk to the national highway i
Externí odkaz:
https://doaj.org/article/b11a61e613cf40d48918b2440926772f
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 124, Iss , Pp 103542- (2023)
Accurate landslide segmentation is crucial for obtaining damage information in disaster mitigation and relief efforts. This study aims to develop a deep learning network for accurate point cloud landslide segmentation. The proposed dynamic graph atte
Externí odkaz:
https://doaj.org/article/ba8370e82b77454a89de102f04c87c05
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 124, Iss , Pp 103521- (2023)
Accurate landslide detection is essential for disaster mitigation and relief. In this study, we develop a feature enhancement framework that integrates attention and multiscale mechanisms with U-Net (AMU-Net) for landslide detection. The framework ha
Externí odkaz:
https://doaj.org/article/4567271bbc75474caa3d5ddfd8349790
Publikováno v:
Water, Vol 16, Iss 7, p 923 (2024)
Machine learning (ML) has become increasingly popular in the prediction of debris flow occurrence, but the various ML models utilized as baseline predictors reported in previous studies are typically limited to individual case bases. A comprehensive
Externí odkaz:
https://doaj.org/article/cca4e5d8bb8d4a24847d7753b37bd81d
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 13, Iss 1, Pp 2777-2795 (2022)
The magnitude and frequency of mountain hazards will continue to increase because of climate change especially in rural mountainous areas, which have not received much attention. In this article, a debris flow disaster chain in Southwest China caused
Externí odkaz:
https://doaj.org/article/ba4cb6961a6e4566b92988eda0fc3e30
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022)
Abstract The construction of check dams is an important measure to prevent soil erosion on the Loess Plateau and reduce the amount of sediment entering the Yellow River. Based on an analysis of the current situation of soil and water conservation on
Externí odkaz:
https://doaj.org/article/5d6cdb3008c94076a37a7b005410cec5
Publikováno v:
Remote Sensing, Vol 15, Iss 15, p 3892 (2023)
This study explored the applicability of TRMM, TRMM nonlinear downscaling, and ANUSPLIN (ANU) interpolation of three different types of precipitation data to define regional-scale rainfall-triggered landslide thresholds. The spatial resolution of TRM
Externí odkaz:
https://doaj.org/article/472caa4aeeda415ab6f85767e8dfa247
Publikováno v:
Frontiers in Environmental Science, Vol 10 (2022)
In the Kuchlak Sub-Basin (Pakistan), groundwater is overexploited, resulting in growing stress on groundwater resources. The water table level has declined rapidly due to intensive pumping. Artificial recharge methods and good management strategies a
Externí odkaz:
https://doaj.org/article/ee93771487f24b16bd4ac08d2663e12f