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The rapid and accurate extraction of wide-area coseismic landslide locations is critical in earthquake emergencies. At present, the extraction of coseismic landslides is mainly based on post-earthquake site investigation or the interpretation of human–computer interactions based on remote sensing images. However, the identification efficiency is low, which seriously delays the earthquake emergency response. On the basis of the available multisource and multiscale remote sensing data, numerous studies have been carried out on the methods of coseismic landslide extraction, such as pixel analysis, object-oriented analysis, change detection, and machine learning. However, the effectiveness of coseismic landslide extraction was low in wide areas with complex topographic and geomorphic backgrounds. Therefore, this paper offers a comprehensive study of the factors influencing coseismic landslides and researches rapid and accurate wide-area coseismic landslide extraction methods with multisource remote sensing and geoscience technology. These techniques include digital elevation modeling (DEM) and its derived slopes and aspects. An embedded multichannel spectral–topographic feature fusion model for coseismic landslide extraction based on DeepLab V3+ is proposed, and a knowledge-enhanced deep learning information extraction method integrating geological knowledge is formed. Using the Jiuzhaigou Ms7.0 earthquake (seismic intensity VIII) in Sichuan Province, China, a comparison of landslide extraction models and strategies is carried out. The results show that the model proposed in this paper achieves the best balance in the accuracy and efficiency of wide-area extractions. Using multiple feature data of coseismic landslides, the problem of mixed pixels is solved. The rate of the misidentification of landslides as clouds, snow, buildings, and roads is significantly lower than in other methods. The identified landslide boundaries are smoother and more accurate, and the connectivity is better. Compared with other methods, ours can more accurately eliminate landslides not triggered by the Jiuzhaigou earthquake. While using the image block strategy to ensure extraction efficiency, it also improves the extraction accuracy of wide-area coseismic landslides in complex backgrounds. [ABSTRACT FROM AUTHOR]

Objectives: On 5th September 2022, an Ms 6.8 earthquake struck the Luding County, Ganzi Prefecture, Sichuan Province, China. This earthquake triggered extensive geological hazards in the moun‐ tainous area, leading to serious casualties. Rapidly and accurately obtaining the spatial distribution of the in‐ duced geological hazards is crucial for emergency decision-making and rescue after an earthquake. Methods: Based on the global coseismic landslide database and deep learning algorithm, this paper built a near real-time prediction model of spatial distribution probability of coseismic landslides, and obtained the prediction results of the geological hazards induced by the Luding earthquake within 2 hours after the event. Through the post-earthquake unmanned aerial vehicle （UAV） and satellite remote sensing images, ma‐ chine learning and deep learning algorithms were used to realize the automated recognition of large-scale geological hazards. A total of 3 633 earthquake-induced landslides with an area of 13.78 km2 were interpreted. Finally, the model was optimized by integrating these landslide data, and the prediction results of coseismic landslides with a broader area and higher accuracy were achieved. Results: The results show that the coseis‐ mic landslide prediction model can realize a rapid capture of spatial distribution of post-earthquake geologi‐ cal hazards, filling the blank period before the acquisition of post-earthquake remote sensing images and providing support for post-disaster emergency rescue. Conclusions: Intelligent identification technologies based on UAV and satellite remote sensing images are effective means to rapidly obtain the vital informa‐ tion of large-scale geological hazards. The achievements obtained in this paper played an important role in the emergency rescue after the Luding earthquake. [ABSTRACT FROM AUTHOR]

Landslides which occur at unexpected locations and times, and which have unrecognizable risk areas, often cause mass casualties. Despite there being few preceding landslides in the same season, the catastrophic Qijiaba landslide occurred in the new government-selected settlement of Wufu village, Sichuan Province, China, on September 5, 2021, killing three people and destroying 13 houses. In this paper, the landslide features and causes are discussed based on field investigations, remote sensing interpretations, and laboratory-based soil experiments. The results showed that it was a typical soil landslide within a geomorphic hollow of the slope with a volume of 20,230 m3, recurrence interval of at least 69 years, and runout distance of 137.5 m. This type of landslide is difficult to predict. The soil strength in the sliding surface decreased suddenly as the water content increased, and the soil's rich clay mineral and high clay particle contents determined its high mobility (Heim's ratio of 0.44). The occurrence time of the Qijiaba landslide lagged 1.3 days behind the peak rainfall, and the 2-day cumulative rainfall was a crucial factor in the landslide occurrence. Finally, the large water confluence area behind the main scarp was determined to be 3.9 times the area of the landslide itself, thereby providing abundant water conditions to induce failure. The factors underlying the casualties included inappropriate site selection, insufficient awareness of potential landslides, and failure to evacuate in time. The results of this study provide a basis for future prevention, mitigation, and early warning of similar landslides. [ABSTRACT FROM AUTHOR]

On August 20, 2019, at 2 a.m., a disastrous debris flow occurred in Chediguan gully in Yinxing town, China. The debris flow destroyed the drainage groove and the bridge at the exit of the gully. In addition, the debris flow temporarily blocked the Minjiang River during the flood peak, flooding the Taipingyi hydropower station 200 m upstream and leaving two plant workers missing. To further understand the activity of the debris flow after the Wenchuan earthquake, the characteristics of this debris flow event were studied. Eleven years after the Wenchuan earthquake, a disastrous debris flow still occurred in the Chediguan catchment, causing more severe losses than those of earlier debris flows. In this paper, the formation mechanism and dynamic characteristics of this debris flow event are analysed based on a drone survey, high-definition remote sensing interpretations and other means. The catastrophic debris flow event indicates that debris flows in the Wenchuan earthquake area are still active. A large amount of dredging work in the main gully could effectively reduce the debris flow risk in the gully. In addition, it is also important to repair or rebuild damaged mitigation measures and to establish a real-time monitoring and early warning system for the high-risk gully. [ABSTRACT FROM AUTHOR]

A rainfall-induced catastrophic landslide occurred at UTC + 8 03:50 local time on August 21, 2020, in Zhonghaicun village (N 29°20′30″, E 102°41′40″), Hanyuan County, Sichuan Province, China, resulting in damage to 6 buildings and 9 casualties (7 fatalities and 2 missing). In this paper, multitemporal remote sensing images, comprehensive field investigations, drilling holes, deformation monitoring, and numerical simulations are used to identify the main characteristics and the formation mechanism of the event. The results showed that long-term intensive rainfall in 2020 played an essential role in the failure of the Zhonghaicun landslide and that strong earthquakes in Wenchuan County (Ms 8.0, on 12 May 2008) and Lushan County (Ms 7.0, on 20 April 2013) also exerted a negative influence on the evolution process of this catastrophic landslide. Several traction zones appeared after the occurrence of the landslide. Fortunately, no evident deformation was observed and these slopes were in a stable state. In addition, a large-scale potential sliding slope, four small-scale landslides, and a debris flow were found in the study area. [ABSTRACT FROM AUTHOR]

The Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) mission core satellite provides the new-generation global observation of rain since 2014. The main objective of this paper is to evaluate the suitability and limitation of GPM-DPR level-2 products over China. The DPR rain rate products are compared with rain gauge data during the summers of 5 years (2014-18). The ground observation network is composed of more than 50 000 rain gauges. The DPR precipitation products for all scans (DPR_NS, DPR_MS, and DPR_HS) generally underestimate rain rates. However, DPR_MS agrees better with gauge estimates thanDPR_NS and DPR_HS, yielding the lowest mean error, systematic deviation, and highest Pearson correlation coefficient. In addition, all three swath types show obvious overestimation over gauge estimates between 0.5 and 1mmh-1 and underestimation when gauge estimates are larger than 1mmh-1. The DPR_HS and DPR_MS agree better with gauge estimates below and above 2.5mmh-1, respectively. A deeper investigation was carried out to analyze the variation of DPR_MS's performance with respect to terrains over China. An obvious underestimation, relative to gauge estimates, occurs in Tibetan Plateau while a slight overestimation occurs in the North China Plain. Furthermore, our comprehensive analysis suggests that in Sichuan Basin, the DPR_MS exhibit the best agreement with gauge estimates. [ABSTRACT FROM AUTHOR]

Seismic landslides are the most common and highly destructive earthquake-triggered geological hazards. They are large in scale and occur simultaneously in many places. Therefore, obtaining landslide information quickly after an earthquake is the key to disaster mitigation and relief. The survey results show that most of the landslide-information extraction methods involve too much manual participation, resulting in a low degree of automation and the inability to provide effective information for earthquake rescue in time. In order to solve the abovementioned problems and improve the efficiency of landslide identification, this paper proposes an automatic landslide identification method named improved U-Net model. The intelligent extraction of post-earthquake landslide information is realized through the automatic extraction of hierarchical features. The main innovations of this paper include the following: (1) On the basis of the three RGB bands, three new bands, DSM, slope, and aspect, with spatial information are added, and the number of feature parameters of the training samples is increased. (2) The U-Net model structure is rebuilt by adding residual learning units during the up-sampling and down-sampling processes, to solve the problem that the traditional U-Net model cannot fully extract the characteristics of the six-channel landslide for its shallow structure. At the end of the paper, the new method is used in Jiuzhaigou County, Sichuan Province, China. The results show that the accuracy of the new method is 91.3%, which is 13.8% higher than the traditional U-Net model. It is proved that the new method is effective and feasible for the automatic extraction of post-earthquake landslides. [ABSTRACT FROM AUTHOR]