Earthquake induced landslides are one of the most severe geo-environmental hazards that cause enormous damage to infrastructure, property, and loss of life in Nuweiba area. This study developed a model for mapping the...Earthquake induced landslides are one of the most severe geo-environmental hazards that cause enormous damage to infrastructure, property, and loss of life in Nuweiba area. This study developed a model for mapping the earthquake-induced landslide susceptibility in Nuweiba area in Egypt with considerations of geological, geomorphological, topographical, and seismological factors. An integrated approach of remote sensing and GIS technologies were applied for that target. Several data sources including Terra SAR-X and SPOT 5 satellite imagery, topographic maps, field data, and other geospatial resources were used to model landslide susceptibility. These data were used specifically to produce important thematic layers contributing to landslide occurrences in the region. A rating scheme was developed to assign ranks for the thematic layers and weights for their classes based on their contribution in landslide susceptibility. The ranks and weights were defined based on the knowledge from field survey and authors experiences related to the study area. The landslide susceptibility map delineates the hazard zones to three relative classes of susceptibility: high, moderate, and low. Therefore, the current approach provides a way to assess landslide hazards and serves for geo-hazard planning and prediction in Nuweiba area.展开更多
Atmospheric turbulence degrades the performance of free-space optical(FSO)communication and remote sensing systems by introducing phase and intensity distortions.While a majority of research focuses on mitigating thes...Atmospheric turbulence degrades the performance of free-space optical(FSO)communication and remote sensing systems by introducing phase and intensity distortions.While a majority of research focuses on mitigating these effects to ensure robust signal transmission,an underexplored alternative is to leverage the transformation of structured light to characterize the turbulent medium itself.Here,we introduce a deep learning framework that fuses post-propagation intensity speckle patterns and orbital angular momentum(OAM)spectral data for atmospheric turbulence parameter inference.Our architecture,based on a modified InceptionNet backbone,is optimized to extract and integrate multi-scale features from these distinct optical modalities.This multimodal approach achieves validation accuracies exceeding 80%,substantially outperforming conventional single-modality baselines.The framework demonstrates high inference accuracy and enhanced training stability across a broad range of simulated turbulent conditions,quantified by varying Fried parameters(r_(0))and Reynolds numbers(Re).This work presents a scalable and data-efficient method for turbulence characterization,offering a pathway toward robust environmental sensing and the optimization of dynamic FSO systems.展开更多
基金the Egyptian Ministry of Higher Education and Scientific Research
文摘Earthquake induced landslides are one of the most severe geo-environmental hazards that cause enormous damage to infrastructure, property, and loss of life in Nuweiba area. This study developed a model for mapping the earthquake-induced landslide susceptibility in Nuweiba area in Egypt with considerations of geological, geomorphological, topographical, and seismological factors. An integrated approach of remote sensing and GIS technologies were applied for that target. Several data sources including Terra SAR-X and SPOT 5 satellite imagery, topographic maps, field data, and other geospatial resources were used to model landslide susceptibility. These data were used specifically to produce important thematic layers contributing to landslide occurrences in the region. A rating scheme was developed to assign ranks for the thematic layers and weights for their classes based on their contribution in landslide susceptibility. The ranks and weights were defined based on the knowledge from field survey and authors experiences related to the study area. The landslide susceptibility map delineates the hazard zones to three relative classes of susceptibility: high, moderate, and low. Therefore, the current approach provides a way to assess landslide hazards and serves for geo-hazard planning and prediction in Nuweiba area.
基金111 Project(B17035)National Natural Science Foundation of China(U20B2059,62575227,62231021,61621005,62201613)+1 种基金Shanghai Aerospace Science and Technology Innovation Foundation(SAST-2022-069)Fundamental Research Funds for the Central Universities(ZYTS25121).
文摘Atmospheric turbulence degrades the performance of free-space optical(FSO)communication and remote sensing systems by introducing phase and intensity distortions.While a majority of research focuses on mitigating these effects to ensure robust signal transmission,an underexplored alternative is to leverage the transformation of structured light to characterize the turbulent medium itself.Here,we introduce a deep learning framework that fuses post-propagation intensity speckle patterns and orbital angular momentum(OAM)spectral data for atmospheric turbulence parameter inference.Our architecture,based on a modified InceptionNet backbone,is optimized to extract and integrate multi-scale features from these distinct optical modalities.This multimodal approach achieves validation accuracies exceeding 80%,substantially outperforming conventional single-modality baselines.The framework demonstrates high inference accuracy and enhanced training stability across a broad range of simulated turbulent conditions,quantified by varying Fried parameters(r_(0))and Reynolds numbers(Re).This work presents a scalable and data-efficient method for turbulence characterization,offering a pathway toward robust environmental sensing and the optimization of dynamic FSO systems.
