Search

Objectives：In mid- to late- April 2024, an extreme heavy rainfall event occurred in Shaoguan City, Guangdong Province, inducing a large number of landslides in Jiangwan Town, Shaoguan. People lost connection with the outside world for nearly 36 hours, which aroused widespread social concern. Rapidly and accurately identifying the basic characteristics of landslides, development and distribution patterns and formation conditions is crucial for disaster emergency decision-making and risk elimination and disposal. Methods: Using the post-disaster optical remote sensing images and combining with deep learning model, the rainfall-induced landslides in Jiangwan Town, Shaoguan, were quickly and automatically identified. Results:After manually calibration, a total of 1 192 landslides were deciphered, with a total area of about 3.14 km². The scale of the landslides was dominated by small and medium-sized landslides, which were mainly distributed as an aggregated belt along the river in the northeast-southwest direction, with a significant characteristic of concentrated occurrence. Spatial statistical analysis showed that the landslides were mainly distributed on concave slopes with slopes of 10°-30° in the range of 200-300 m elevation. Further quantitative analysis of the geomorphic controlling factors of landslides using the random forest model and SHAP theory reveals that different topographic and geomorphic factors have different degrees of nonlinear effects on landslide formation, and that multiple factors such as elevation, slope, and catchment conditions are coupled to jointly control the formation of landslides. Conclusions: This paper highlights the great advantage of deep learning-based intelligent identification and analysis technology in the emergency investigation and formation conditions analysis of landslide disasters, which can provide important technical support for the rapid assessment of disaster losses and risk identification. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: Lanzhou New Area is an important window for the development of northwest China. In recent years, the large‐scale construction of mountain excavation and city construction project has led to different degrees of land subsidence, which has seriously threatened the economic and social development of the new area. Methods: Taking Lanzhou New Area as the research object, this paper uses small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) to process 58 ascending Sentinel-1A image data covering the study area from May 2017 to February 2021. The distribution characteristics and spatio temporal evolution of land subsidence in the study area were obtained, and the SBAS-InSAR measurement results were verified by field investigation. Then the related factors affecting land subsidence were discussed. Results: The results show that land subsidence in Lanzhou New Area is mainly concentrated in mountain excavation and city construction area, with an area of about 33.5 km² and a maximum subsidence rate of-68 mm/a. In space, with the development of excavation and filling projects, the land subsidence develops to the southeast of the new district, and the land surface presents a long‐term continuous settlement process. Conclusions: Land subsidence is closely related to many factors such as mountain excavation and city construction project, thickness of excavation and filling, geology and road. Specifically, mountain excavation and city construction project control the spatial extent of land subsidence, and then the filling thickness control the magnitude of land subsidence. The nature of artificial fill is an intrinsic factor causing land subsidence, while road construction will also accelerate the process of land subsidence. The research results can provide scientific basis for the sustainable and safe development of the new district and urban planning and construction. [ABSTRACT FROM AUTHOR]

Big data has provided new opportunities for landslide research, but in-depth mining of landslide data is still limited due to complex data types, diverse semantic relationships, and unclear sharing mechanisms. And it is difficult to give full play to the advantages of big data in landslide research. In this paper, a landslide knowledge graph construction method for the field of engineering geology is proposed, which extracts, fuses and structures multi-source heterogeneous landslide knowledge to realize the inquiry, correlation and reasoning of landslide knowledge big data. The combination of top-down and bottom-up methods was used to divide the concept and ontology of landslides. A knowledge graph model layer was formed based on 10 categories of knowledge, including landslide field investigation, landslide evaluation, landslide type, landslide geomorphological characteristics, landslide morphological characteristics, landslide disaster information, states of activity of landslides, landslide genesis mechanism, landslide stability analysis method, and landslide prevention and control measures. The knowledge graph model layer includes concept layer, attribute layer, relationship layer, rule layer, and instance layer, and it is constructed by extracting landslide knowledge information from broad data sources, establishing semantic networks and integrating redundant knowledge. The Neo4j platform is used to store landslide knowledge, realize knowledge visualization and retrieval, and provide new ideas and methods for landslide mechanism research and disaster prevention and mitigation. The proposed construction method of landslide knowledge graph can be extended to the research of other types of disaster knowledge graph, and is linked with other disciplines to promote the deep intersection and integration of disciplines. [ABSTRACT FROM AUTHOR]

Objectives: As one of the precursor features of slope deformation, surface cracks can provide forecast information for the early identification of geological hazards and the determination of motion instability characteristics. Affected by terrain conditions, the efficiency of manual investigation is low, and it is difficult to solve the problem of size effect and noise filtering of cracks in complex backgrounds using a single remote sensing data identification. In order to efficiently collect the location and geometry information of surface fractures on deformed slopes, the main purpose is to develop a method system for automatic crack identification and information statistics based on remote sensing data, which is suitable for large-scale slope surfaces. Methods: Using the high-resolution three⁃dimensional point cloud and orthophoto image generated by autonomous terrain-following flight technology as the data source, the automatic extraction of cracks and the statistical research on the deformed slope of the reservoir bank of Baihetan Hydropower Station are carried out. The initial automatic identification of slope cracks is completed by using six algorithm models with point cloud's roughness, slope, dispersion and digital image's pixel gradient, gray value and RGB (red green blue) value as identification features respectively. Completed receiver operating characteristic curve (ROC) tests of different models and determined segmentation thresholds. The initial extraction results are processed by morphological repair and three filtering algorithms of crack direction, length and frequency based on density-based spatial clustering of applications with noise (DBSCAN) algorithm index to deal with background noise. The two-category evaluation index is used to analyze the pros and cons of the 6 filtered crack extraction results, and for the effect of crack size, the optimal detection model after data fusion is obtained. Based on the crack skeleton and outline, 6 quantitative characteristic indicators of quantity, length, width, direction, dispersion, and crack density are automatically calculated. Results: Starting from the fracture image characteristics of different scales, 6 different fracture identification models are used and ROC test is carried out, and the area under the curve values are all between 0.6 and 0.85. The highest F1 value of the model after fusion is 0.835 0, which can better meet the actual engineering needs. The morphological repair and filtering algorithm proposed can effectively reduce the background noise of cracks, improve the overall accuracy of the model, and can remove up to 82.7% of the noise with minimal crack distortion. The density clustering algorithm of DBSCAN is used to complete the fracture pixel classification and quantity statistics, and according to the ratio of pixel size and actual distance, the algorithm automatically completes the information acquisition of fractures, which can provide technical support for quantitative description and evaluation of fracture characteristics. Various types of crack identification algorithms used in this paper have different advantages and disadvantages and applicable situations, and the optimal combination relationship can be selected according to the field conditions with different characteristics. Conclusions: In this study, a method for automatic crack extraction and information statistics is constructed in a large-scale, multi-scale complex deformation slope scene, which can meet the needs of actual production operations. At the same time, it has important practical significance to further promote the early identification of geological disasters and intelligent monitoring and early warning. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: Traditional remote sensing methods are often limited by the complex terrain and the hardware equipment, which often leads to in-situ measurements that cannot comprehensively, accurately and quickly obtain the slope rock structure data sets. Unmanned aerial vehicle (UAV) oblique photogrammetry technology can obtain comprehensive information on rock structure. Methods: We describe a method for acquiring, visualizing, and extracting three-dimensional information of high slope rock structure based on the dense surface points. The oblique photogrammetric data acquired with UAV is considered and a multi-rotor drone with single-lens is used for aerial surveys to reconstruct the three-dimensional point cloud model of the high slope. The Hough normal algorithm and the HSV (hue, saturation, value) algorithm are used to build a visual three-dimensional rock mass structure model and the model is spherical k-means clustering to complete the automatic extraction of rock structure. Results: From the three-dimensional rock structure analysis, it is possible to quickly extract rock structure characteristics such as orientation, spacing and trace length by rendering the geometry of the three-dimensional slope face. Conclusions: Further research is being carried out in order to build blocky slope masses and identify the kinematic instability of the high limestone cliffs. [ABSTRACT FROM AUTHOR]

Objectives: The knowledge of regional landslides detection plays a fundamental role in the landslide risk management. However, most of that recognition was taken manually in the past, which is rather time- and labor- consuming. As the development of technologies of remote sensing and artificial intelligence, the automatic detection of landslides becomes possible. The previous researches relative to the automatic detection of landslides utilized the machine learning methods to detect these new landslides which were significantly distinguished from their context. Compared to those landslides, the detection of old loess landslides that are not distinct from their context is more challenged. We explore the deep learning to automatically detect the old loess landslides. Methods: Firstly, we build a loess landslide database consists of 2 498 which are interpreted from the Google Earth images by experts. Then, we divide the database into three datasets for training, validation and test. Finally, we train Mask R-CNN object detection module with the training dataset, choose the best model by the validation dataset, and apply the best model to the test dataset. Results: The test results of model performance show a precision of 0.56, a recall of 0.72, and a F1-score of 0.63.Conclusions: The results indicate that Mask R-CNN is a robust method even for the detection of loess landslides that are unapparent from the context, and deep learning can provide the possibility for rapid and accurate regional geo-hazard investigation. [ABSTRACT FROM AUTHOR]

Objectives: The "mountain excavation and city construction" in Yan'an New District is the largest geotechnical project in the collapsible loess gully region. Large-scale land creation, engineering construction and complex engineering geological conditions has caused a large number of uneven land subsidence, which is posing an increasing threat to the stability of urban infrastructure and public safety. Methods: The ascending Sentinel-1A data stacks obtained during December 2017 to December 2018 were analyzed using the time series interferometric synthetic aperture radar (InSAR) technique, and the distribution characteristics of the post-construction land subsidence of the mountain excavation and city construction in Yan'an New District were obtained. The reliability of the detection results of InSAR technology was demonstrated by field investigation. Finally, based on the deformation monitoring results, the main influencing factors of land subsidence distribution were analyzed. Results: (1) The results show that significant uneven land subsidence formed after the mountain excavation and city construction in Yan'an New District is mainly distributed in the filling area, and the maximum deformation rate reaches up to 120.1 mm/a. Field investigation validates the reliability and accuracy of the deformation results. (2) The remodeling of original loess and the change of its physical properties during the filling process is the main intrinsic factors of land subsidence in the filling area. Large-scale land creation determines the distribution of land subsidence, and the thickness of the filling are the main controlling factor of the distribution and size of land subsidence. In addition, human activities and changes in the geo-environment will also accelerate the development of land subsidence. Conclusions: In order to achieve the early detection of potential geo-hazards and active risk prevention in the site selection and feasibility study stage of mega engineering projects in the Loess Plateau, the time-series InSAR technology can provide an effective technical that allows for the early detection of potential geo-hazards such as land subsidence and landslides, and provide a scientific basis for further monitoring and warning, planning and construction, geologic disaster prevention. [ABSTRACT FROM AUTHOR]

Objectives: In China, geohazards are wide-ranging. Traditional artificial investigations have found nearly three hundred thousand locations of potential geohazards. However, the recent occurred cata-strophic geohazards are not within these determined locations. Widely identification of potential geohazards become one of the most important jobs for geohazard prevention and mitigation. Methods: We propose some suggestions to promote early identification for potential geohazards. Results: (1) Recently, various re- mote sensing techniques play an significant role in geohazard identification, but each technique has its limitation to recognize geohazards with different types and characteristics. Only integrated technologies, mutual complementation and verification, can effectively solve the problem. (2) Combination between traditional geological surveys and modern technologies (LiDAR, aerial and semi-aerial geophysical exploration, etc.) can improve the efficiency and accuracy for identification of the most difficult and unstable slops. (3) The deep machine learning is expected to realize the intelligent automatic identification of geohazards. Currently, it shows good performance in new geohazards with significant spectral and texture characteristics, while the accuracy of automatic identification for other types, such as ancient landslides and normal potential geo-hazards, is still not enough. Conclusions: More efforts are in urgent need for further research in related fields. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)