Visualization of Quality of Software Requirements Specification Using Digital Elevation Model

2017 ◽  
pp. 209-215
Author(s):  
Diding Adi Parwoto ◽  
Takayuki Omori ◽  
Hiroya Itoga ◽  
Atsushi Ohnishi
Keyword(s):  
Digital Elevation Model ◽  
Requirements Specification ◽  
Software Requirements ◽  
Digital Elevation ◽  
Software Requirements Specification ◽  
Elevation Model

scholarly journals Digital elevation model validation with no ground control: application to the topodata dem in Brazil

2014 ◽  
Vol 20 (2) ◽  
pp. 467-479 ◽  
Author(s):  
Laurent Polidori ◽  
Mhamad El Hage ◽  
Márcio De Morisson Valeriano
Keyword(s):  
Digital Elevation Model ◽  
Visual Analysis ◽  
Ground Control ◽  
Ground Control Points ◽  
Geostatistical Approach ◽  
Digital Elevation ◽  
Elevation Model ◽  
Control Application ◽  
Statistical Indicators

Digital Elevation Model (DEM) validation is often carried out by comparing the data with a set of ground control points. However, the quality of a DEM can also be considered in terms of shape realism. Beyond visual analysis, it can be verified that physical and statistical properties of the terrestrial relief are fulfilled. This approach is applied to an extract of Topodata, a DEM obtained by resampling the SRTM DEM over the Brazilian territory with a geostatistical approach. Several statistical indicators are computed, and they show that the quality of Topodata in terms of shape rendering is improved with regards to SRTM.

scholarly journals Development of an automatic delineation of cliff top and toe on very irregular planform coastlines (CliffMetrics v1.0)

2018 ◽  
Author(s):  
Andres Payo ◽  
Bismarck Jigena Antelo ◽  
Martin Hurst ◽  
Monica Palaseanu-Lovejoy ◽  
Chris Williams ◽  
...  
Keyword(s):  
Great Britain ◽  
Digital Elevation Model ◽  
Model Output ◽  
Dem Resolution ◽  
M 10 ◽  
Digital Elevation ◽  
Elevation Model

Abstract. We describe a new algorithm that automatically delineates the cliff top and toe of a cliffed coastline from a Digital Elevation Model (DEM). The algorithm builds upon existing methods but is specifically designed to resolve very irregular planform coastlines with many bays and capes, such as parts of the coastline of Great Britain. The algorithm automatically and sequentially delineates and smooth shoreline vectors, generates orthogonal transects and elevation profiles with a minimum spacing equal to the DEM resolution, and extracts the position and elevation of the cliff top and toe. Outputs include the non-smoothed-raster and smoothed-vector coastline, normals to the coastline- (as vector shapefiles), xyz profiles (as comma-separated-value files), and the cliff top and toe (as point shape files). The algorithm also automatically assesses the quality of the profile and omits low-quality profiles (i.e. extraction of cliff top and toe is not possible). The performance of the proposed algorithm is compared with an existing method, which was not specifically designed for very irregular coastlines, and to hand-digitized boundaries by numerous professionals. Also we assess the reproducibility of the results using different DEM resolutions (5 m, 10 m and 50 m), different user defined parameter-sets related to the degree of coastline smoothing, and the threshold used to identify the cliff top and toe. The model output sensitivity is found to be smaller than hand-digitized uncertainty. Code and a manual are publicly available on a github repository.

scholarly journals The Impact of Quality of Digital Elevation Models on the Result of Landslide Susceptibility Modeling Using the Method of Weights of Evidence

Geosciences ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 488
Author(s):  
Mirosław Kamiński
Keyword(s):  
Digital Elevation Model ◽  
Roc Curve ◽  
Landslide Susceptibility ◽  
Weights Of Evidence ◽  
Identification System ◽  
Susceptibility Modeling ◽  
Digital Elevation ◽  
Elevation Model ◽  
The Impact

The paper discusses the impact that the quality of the digital elevation model (DEM) has on the final result of landslide susceptibility modeling (LSM). The landslide map was developed on the basis of the analysis of archival geological maps and the Light Detection and Ranging (LiDAR) digital elevation model. In addition, complementary field studies were conducted. In total, 92 landslides were inventoried and their degree of activity was assessed. An inventory of the landslides was prepared using a 1-m-LiDAR DEM and field research. Two digital photogrammetric elevation models with an elevation pixel resolution of 20 m were used for landslide susceptibility modeling. The first digital elevation model was obtained from a LiDAR point cloud (DEM–airborne laser scanning (ALS)), while the second model was developed based on archival digital stereo-pair aerial images (DEM–Land Parcel Identification System (LPIS)). Both models were subjected to filtration using a Gaussian low-pass filter to reduce errors in their elevation relief. Then, using ArcGIS software, a differential model was generated to illustrate the differences in morphology between the models. The maximum differences in topographic elevations between the DEM–ALS and DEM–LPIS models were calculated. The Weights-of-Evidence model is a geostatistical method used for the landslide susceptibility modeling. Six passive factors were employed in the process of susceptibility generation: elevation, slope gradient, exposure, topographic roughness index (TRI), distance from tectonic lines, and distance from streams. As a result, two landslide susceptibility maps (LSM) were obtained. The accuracy of the landslide susceptibility models was assessed based on the Receiver Operating Characteristic (ROC) curve index. The area under curve (AUC) values obtained from the ROC curve indicate that the accuracy of classification for the LSM–DEM–ALS model was 78%, and for the LSM–LPIS–DEM model was 73%.

scholarly journals Experimental Analysis of Srtm Model by Image Processing and Geostatistical Methods

2018 ◽  
Vol 7 (4.7) ◽  
pp. 250 ◽  
Author(s):  
M. V. Kuzyakina ◽  
D. A. Gura ◽  
Yu. A. Mishchenko ◽  
D. A. Gordienko
Keyword(s):  
Image Processing ◽  
Digital Elevation Model ◽  
Experimental Analysis ◽  
Geostatistical Methods ◽  
Digital Elevation ◽  
Different Types ◽  
Elevation Model ◽  
Bicubic Interpolation

This article compares the image processing and geostatistical methods of GIS. They proposed the application of these methods to restore the quality of the well-known digital elevation model SRTM degraded sections using the example of the Krasnodar Territory. The conclusions are drawn also about the quality of modeling for test sites with different types of relief – flat, hilly and mountain. The best results were achieved for the method of bicubic interpolation. 

scholarly journals Development of an automatic delineation of cliff top and toe on very irregular planform coastlines (CliffMetrics v1.0)

2018 ◽  
Vol 11 (10) ◽  
pp. 4317-4337 ◽  
Author(s):  
Andres Payo ◽  
Bismarck Jigena Antelo ◽  
Martin Hurst ◽  
Monica Palaseanu-Lovejoy ◽  
Chris Williams ◽  
...  
Keyword(s):  
Great Britain ◽  
Digital Elevation Model ◽  
Model Output ◽  
Dem Resolution ◽  
Digital Elevation ◽  
Elevation Model

Abstract. We describe a new algorithm that automatically delineates the cliff top and toe of a cliffed coastline from a digital elevation model (DEM). The algorithm builds upon existing methods but is specifically designed to resolve very irregular planform coastlines with many bays and capes, such as parts of the coastline of Great Britain. The algorithm automatically and sequentially delineates and smooths shoreline vectors, generates orthogonal transects and elevation profiles with a minimum spacing equal to the DEM resolution, and extracts the position and elevation of the cliff top and toe. Outputs include the non-smoothed raster and smoothed vector coastlines, normals to the coastline (as vector shape files), xyz profiles (as comma-separated-value, CSV, files), and the cliff top and toe (as point shape files). The algorithm also automatically assesses the quality of the profile and omits low-quality profiles (i.e. extraction of cliff top and toe is not possible). The performance of the proposed algorithm is compared with an existing method, which was not specifically designed for very irregular coastlines, and to manually digitized boundaries by numerous professionals. Also, we assess the reproducibility of the results using different DEM resolutions (5, 10 and 50 m), different user-defined parameter sets related to the degree of coastline smoothing, and the threshold used to identify the cliff top and toe. The model output sensitivity is found to be smaller than the manually digitized uncertainty. The code and a manual are publicly available on a GitHub repository.

scholarly journals Challenges and Opportunities for UAV-Based Digital Elevation Model Generation for Flood-Risk Management: A Case of Princeville, North Carolina

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3843 ◽  
Author(s):  
Leila Hashemi-Beni ◽  
Jeffery Jones ◽  
Gary Thompson ◽  
Curt Johnson ◽  
Asmamaw Gebrehiwot
Keyword(s):  
North Carolina ◽  
Natural Disasters ◽  
Digital Elevation Model ◽  
Flood Risk ◽  
Water Surface ◽  
Response Planning ◽  
Digital Elevation ◽  
Challenges And Opportunities ◽  
Elevation Model

Among the different types of natural disasters, floods are the most devastating, widespread, and frequent. Floods account for approximately 30% of the total loss caused by natural disasters. Accurate flood-risk mapping is critical in reducing such damages by correctly predicting the extent of a flood when coupled with rain and stage gage data, supporting emergency-response planning, developing land use plans and regulations with regard to the construction of structures and infrastructures, and providing damage assessment in both spatial and temporal measurements. The reliability and accuracy of such flood assessment maps is dependent on the quality of the digital elevation model (DEM) in flood conditions. This study investigates the quality of an Unmanned Aerial Vehicle (UAV)-based DEM for spatial flood assessment mapping and evaluating the extent of a flood event in Princeville, North Carolina during Hurricane Matthew. The challenges and problems of on-demand DEM production during a flooding event were discussed. An accuracy analysis was performed by comparing the water surface extracted from the UAV-derived DEM with the water surface/stage obtained using the nearby US Geologic Survey (USGS) stream gauge station and LiDAR data.

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