scholarly journals Brief Communication: Initializing RAMMS with High Resolution LiDAR Data for Avalanche Simulations

2021 ◽  
Author(s):  
James Dillon ◽  
Kevin Hammonds
Keyword(s):  
High Resolution ◽  
Snow Depth ◽  
Software Tool ◽  
Lidar Data ◽  
Mass Movements ◽  
Flow Volume ◽  
Vegetative Cover ◽  
Avalanche Forecasting ◽  
Simulation Results ◽  
Rapid Mass

Abstract. The Rapid Mass Movements Simulator (RAMMS) is an avalanche dynamics software tool for research and forecasting. Since the model’s conception, the sensitivity of inputs on simulation results has been well-documented. Here, we introduce a new method for initializing RAMMS that can be easily operationalized for avalanche forecasting using high resolution LiDAR data. As a demonstration, hypothetical avalanche simulations were performed while incrementally incorporating semi-automated LiDAR-derived values for snow depth, interface topography, and vegetative cover from field-collected LiDAR data. Results show considerable variation in the calculated runout extent, flow volume, pressure, and velocity of the simulated avalanches when incorporating these LiDAR-derived values.

scholarly journals Multiscale Semantic Feature Optimization and Fusion Network for Building Extraction Using High-Resolution Aerial Images and LiDAR Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2473
Author(s):  
Qinglie Yuan ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Aidi Hizami Alias ◽  
Shaiful Jahari Hashim
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Spatial Information ◽  
Feature Fusion ◽  
Aerial Images ◽  
Semantic Gap ◽  
Superior Performance ◽  
Lidar Data ◽  
Building Extraction ◽  
Hierarchical Features

Automatic building extraction has been applied in many domains. It is also a challenging problem because of the complex scenes and multiscale. Deep learning algorithms, especially fully convolutional neural networks (FCNs), have shown robust feature extraction ability than traditional remote sensing data processing methods. However, hierarchical features from encoders with a fixed receptive field perform weak ability to obtain global semantic information. Local features in multiscale subregions cannot construct contextual interdependence and correlation, especially for large-scale building areas, which probably causes fragmentary extraction results due to intra-class feature variability. In addition, low-level features have accurate and fine-grained spatial information for tiny building structures but lack refinement and selection, and the semantic gap of across-level features is not conducive to feature fusion. To address the above problems, this paper proposes an FCN framework based on the residual network and provides the training pattern for multi-modal data combining the advantage of high-resolution aerial images and LiDAR data for building extraction. Two novel modules have been proposed for the optimization and integration of multiscale and across-level features. In particular, a multiscale context optimization module is designed to adaptively generate the feature representations for different subregions and effectively aggregate global context. A semantic guided spatial attention mechanism is introduced to refine shallow features and alleviate the semantic gap. Finally, hierarchical features are fused via the feature pyramid network. Compared with other state-of-the-art methods, experimental results demonstrate superior performance with 93.19 IoU, 97.56 OA on WHU datasets and 94.72 IoU, 97.84 OA on the Boston dataset, which shows that the proposed network can improve accuracy and achieve better performance for building extraction.

Hillslope Form and Process: History 1960− 2000+

2021 ◽  
pp. M58-2021-8
Author(s):  
Mike Kirkby
Keyword(s):  
Land Surface ◽  
Drainage Density ◽  
Cold Climate ◽  
Lower Atmosphere ◽  
Tectonic Activity ◽  
Significant Advance ◽  
Mass Movements ◽  
Solution Processes ◽  
Process Understanding ◽  
Rapid Mass

AbstractThe study of hillslopes has been dominated by the expansion of studies into process rates and mechanisms. Perhaps the greatest volume of work has been on the ‘wash’ processes of soil erosion, but there has also been significant work on the diffusive mass movements of linear and non-linear ‘creep’ that shape the convexity of hilltops, on more rapid mass movements and on solution processes. There has also been fresh work on distinctive processes in coastal, arid and cold-climate environments.Accompanying and integrated with process understanding, and made possible by ubiquitous computational power, modelling has developed from soluble mathematical simplifications to complex simulations that incorporate much of our understanding of process and climate.Particular topics that have seen significant advance include a more complete understanding of drainage density and texture, and a broadening of interest to encompass the ‘critical zone’ that constructively unifies the land surface with the lower atmosphere, the biosphere and the regolith. There has also been a change of focus towards steeplands, dominated by mass movements, supply limited removal and tectonic activity.Most recently, and now incorporated into the concept of the ‘Anthropocene’, human impact is now receiving increasing attention as we acknowledge its accelerating role in changing landscapes and their relationships.

scholarly journals Bandwidth calculation for VoIP networks based on PSTN statistical model

2010 ◽  
Vol 23 (1) ◽  
pp. 73-88 ◽  
Author(s):  
Zvezdan Stojanovic ◽  
Djordje Babic
Keyword(s):  
Statistical Model ◽  
Software Tool ◽  
Traffic Analysis ◽  
Proper Number ◽  
Simulation Results

This paper shows an analysis how to calculate proper bandwidth for VoIP calls after proper dimensioning of PSTN network. For this purpose, we use Erlang B and extended Erlang B formulae. Further, we have developed a software tool, named Bandwidth Calculator to calculate proper number of the circuits on the PSTN side and after that IP bandwidth. Traffic analysis is conducted for VoIP networks considering impact of many factors on the bandwidth such as: voice codecs, samples, VAD, RTP compression. The results obtained by bandwidth calculator are compared to simulation results and data obtained by measurements. .

scholarly journals Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

2021 ◽  
Vol 11 (18) ◽  
pp. 8365
Author(s):  
Liming Gao ◽  
Lele Zhang ◽  
Yongping Shen ◽  
Yaonan Zhang ◽  
Minghao Ai ◽  
...  
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Implicit Scheme ◽  
Snow Water ◽  
Irtysh River ◽  
Simulation Results ◽  
The Irtysh River

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

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