The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evalu...The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.展开更多
Problematic soils usually cause considerable problems to engineering projects. As an example, soil structure collapse caused by moisture increment or rising underground water level results in huge settlements. This ty...Problematic soils usually cause considerable problems to engineering projects. As an example, soil structure collapse caused by moisture increment or rising underground water level results in huge settlements. This type of problematic soil, named collapsible soil, can cause dramatic problems and should be amended where exists. Today, the use of different techniques for soil reinforcement and soil improvement is widely used to treat soil properties. One of these methods is Deep Soil Mixing (DSM) method. This method becomes more important in the cases of studying and examining collapsible soils. In this research, the settlement of amended collapsible soils, applying deep soil mixing method, is examined. The experiments show that soil amendment using this method, well prevents the settlement of collapsible soils giving rise to bearing capacity.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42090054,41931295)the Natural Science Foundation of Hubei Province of China(2022CFA002)。
文摘The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.
文摘Problematic soils usually cause considerable problems to engineering projects. As an example, soil structure collapse caused by moisture increment or rising underground water level results in huge settlements. This type of problematic soil, named collapsible soil, can cause dramatic problems and should be amended where exists. Today, the use of different techniques for soil reinforcement and soil improvement is widely used to treat soil properties. One of these methods is Deep Soil Mixing (DSM) method. This method becomes more important in the cases of studying and examining collapsible soils. In this research, the settlement of amended collapsible soils, applying deep soil mixing method, is examined. The experiments show that soil amendment using this method, well prevents the settlement of collapsible soils giving rise to bearing capacity.
