Reactivation characteristics and dynamic hazard prediction of an ancient landslide in the east margin of Tibetan Plateau

Wu, Ruian; Zhang, Yongshuang; Guo, Changbao; Yang, Zhihua; Ren, Sanshao; Tong, Bin

doi:10.1007/s12665-018-7741-7

Cited by 12 publications

(3 citation statements)

References 22 publications

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“…These old landslides have typically evolved through the gradual accumulation of rockfalls, resulting in an inverted triangle, conical, or fan-shaped appearance on the plane (Figure 2). Notably, some large and extra-large old landslides have distinctive armchair landforms with clear boundaries, which have now become inhabited areas, serving as the main living places for present-day residents [36,37]. The dataset used in this study comprised Google Earth images with a spatial resolution of 2 m and covered a time span from 2012 to 2022.…”

Section: Study Area and Datasetmentioning

confidence: 99%

Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8

Li,

Ding,

Zhang

et al. 2024

Applied Sciences

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The reactivation of old landslides can be triggered by heavy destructive earthquakes, heavy rainfall, and ongoing human activities, thereby resulting in the occurrence of secondary landslides. However, most existing models are designed for detecting nascent landslides and there are few algorithms for old landslide detection. In this paper, we introduce a novel landslide detection model known as YOLOv8-CW, built upon the YOLOv8 (You Only Look Once) architecture, to tackle the formidable challenge of identifying old landslides. We replace the Complete-IoU loss function in the original model with the Wise-IoU loss function to mitigate the impact of low-quality samples on model training and improve detection recall rate. We integrate a CBAM (Convolutional Block Attention Module) attention mechanism into our model to enhance detection accuracy. By focusing on the southwest river basin of the Sichuan–Tibet area, we collect 558 optical remote sensing images of old landslides in three channels from Google Earth and establish a dataset specifically for old landslide detection. Compared to the original model, our proposed YOLOv8-CW model achieves an increase in detection accuracy of 10.9%, recall rate of 6%, and F1 score from 0.66 to 0.74, respectively. These results demonstrate that our improved model exhibits excellent performance in detecting old landslides within the Sichuan–Tibet area.

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Section: Study Area and Datasetmentioning

confidence: 99%

Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8

Li,

Ding,

Zhang

et al. 2024

Applied Sciences

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“…When engineering construction is conducted in mountainous areas, high platforms or buildings are often developed on the slope to break the original equilibrium state of the slope and the slope can slide downward [6][7][8][9]. During a landslide a large number of cracks arise, such as tensile cracks in the back wall of the landslide, fan-shaped tensile cracks in the front edge, shear cracks in the perimeter of a landslide, bulging, and tension cracks in the landslide [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Deformation Characteristics of Tianjiaba Landslide Induced by Surcharge

Zhang

Wang

Xia

et al. 2021

IJGI

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The Tianjiaba landslide on the Hang Rui expressway was located in the south of Sinan County, Tongren City, Guizhou Province. From the night of 2 July to the morning of 3 July 2011, numerous house cracks, foundation subsidence, and surface cracks suddenly occurred in the Tudiwan and Tianjiaba villages on the north and south sides of the management area. Based on the field geological survey, drilling, pit exploration, surface displacement, fracture observation, and deep displacement monitoring data analysis the deformation characteristics and causes of the Tianjiaba landslide were comprehensively analyzed. In this study, the landslide was divided into three areas, in which fracture group 3 was the most severe. Among the ten inclinometer holes, the sliding surface displacement of No.8 inclinometer hole was the largest, i.e., 25 mm. The sliding surface slopes of fracture groups 1, 2, and 3 were small, i.e., 12.25°, 4.97° and 6.62° to 4.64°, respectively. The results showed that the displacement values of different positions of the landslide were different, and the ground displacement value was larger than the sliding surface displacement value. The maximum displacement of the ground was 242.68 mm, which is larger than the maximum displacement of the inclinometer hole. Because the displacement of the upper part was greater than that of the lower part, bulging cracks formed in the lower part of the landslide. When the displacement of the upper part was less than that of the lower part, tension cracks formed in the lower part of the landslide. According to the surface cracks and displacement values of inclinometer holes on the sliding surface, the landslide was in the initial sliding stage and filling the platform of the management area on the hillside was the main cause of the landslide.

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“…A lo largo del tiempo, se ha evidenciado la prominente modificación de la línea de costa afectada por la erosión y deslizamientos (Tapete et al, 2013;Esposito et al, 2020) causados en ocasiones por el clima húmedo, el incremento del nivel del mar, dirección del oleaje (Budetta et al, 2008;Gerivani & Savari 2019) y las actividades humanas que amenazan a los asentamientos poblacionales situados inadecuadamente en los acantilados (Emery & Kuhn 1982;Wu, Zhang et al, 2018). La erosión en los acantilados costeros se ha definido como un proceso natural producido por el intemperismo, la acción marino y sub-marino que a través del tiempo ha originado un desgaste estructural (Earlie et al, 2018).…”

Section: Introductionunclassified

Coastline modification at worldwide

Alcivar

Mero

Navarrete

et al. 2021

manglar

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La presente indagación bibliográfica pretende sintetizar información de las bases de reseñas de actividades científicas referente a la dinámica de la morfología de los acantilados a nivel mundial, mediante la recopilación de datos como: referencia bibliográfica, ubicación geográfica, método utilizado en la obtención de la tasa de retroceso, tipos de suelos que reflejan factores intrínsecos como las características físico-mecánicas, mineralógicas, geológicas y geomorfológicas, y factores extrínsecos entre los que podemos citar precipitaciones, clima, fuerza de los vientos, temperatura, acciones químicas del medio ambiente y en especial el cambio climático, que intervienen en la modificación de los acantilados de la línea de costa como consecuencias de erosiones sucesivas y deslizamientos. De la información recabada se obtuvo detalles de cinco países europeos, tres asiáticos, tres africanos, siete de América Latina y uno de Oceanía cuyos datos se encuentran plasmados en POWER ArcGIS. Concluyendo que el continente europeo tiene más información porque posee un sistema de satélites que es controlado por un programa

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Reactivation characteristics and dynamic hazard prediction of an ancient landslide in the east margin of Tibetan Plateau

Cited by 12 publications

References 22 publications

Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8

Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8

Deformation Characteristics of Tianjiaba Landslide Induced by Surcharge

Coastline modification at worldwide

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