Geomorphological Characteristics of the Italian Side of Canin Massif (Julian Alps) using Digital Terrain Analysis and Field Observations

2011 ◽  
Vol 40 (2) ◽  
Author(s):  
Tamás Telbisz ◽  
László Mari ◽  
Lénárd Szabó
Keyword(s):  
Terrain Analysis ◽  
Field Observations ◽  
Digital Terrain Analysis ◽  
Digital Terrain ◽  
Julian Alps

scholarly journals A Method of Optimizing Terrain Rendering Using Digital Terrain Analysis

2021 ◽  
Vol 10 (10) ◽  
pp. 666
Author(s):  
Lei Zhang ◽  
Ping Wang ◽  
Chengyi Huang ◽  
Bo Ai ◽  
Wenjun Feng
Keyword(s):  
Large Scale ◽  
Frame Rate ◽  
Terrain Analysis ◽  
Image Pyramid ◽  
Digital Terrain Analysis ◽  
Terrain Rendering ◽  
Digital Terrain ◽  
Elevation Data ◽  
View Frustum Culling ◽  
Elevation Model

Terrain rendering is an important issue in Geographic Information Systems and other fields. During large-scale, real-time terrain rendering, complex terrain structure and an increasing amount of data decrease the smoothness of terrain rendering. Existing rendering methods rarely use the features of terrain to optimize terrain rendering. This paper presents a method to increase rendering performance through precomputing roughness and self-occlusion information making use of GIS-based Digital Terrain Analysis. Our method is based on GPU tessellation. We use quadtrees to manage patches and take surface roughness in Digital Terrain Analysis as a factor of Levels of Detail (LOD) selection. Before rendering, we first regularly partition the terrain scene into view cells. Then, for each cell, we calculate its potential visible patch set (PVPS) using a visibility analysis algorithm. After that, A PVPS Image Pyramid is built, and each LOD level has its corresponding PVPS. The PVPS Image Pyramid is stored on a disk and is read into RAM before rendering. Based on the PVPS Image Pyramid and the viewpoint’s position, invisible terrain areas that are not culled through view frustum culling can be dynamically culled. We use Digital Elevation Model (DEM) elevation data of a square area in Henan Province to verify the effectiveness of this method. The experiments show that this method can increase the frame rate compared with other methods, especially for lower camera flight heights.

Parallelization of Digital Terrain Analysis Algorithms

2013 ◽  
Vol 5 (6) ◽  
pp. 907-916
Author(s):  
Ling Jiang ◽  
Xuejun Liu ◽  
Guoan Tang ◽  
Xiaodong Song ◽  
Kai Liu ◽  
...  
Keyword(s):  
Terrain Analysis ◽  
Digital Terrain Analysis ◽  
Digital Terrain

Digital terrain analysis of a landslide on the loess tableland using high-resolution topography data

Landslides ◽  
2018 ◽  
Vol 16 (3) ◽  
pp. 617-632 ◽  
Author(s):  
Sheng Hu ◽  
Haijun Qiu ◽  
Yanqian Pei ◽  
Yifei Cui ◽  
Wanli Xie ◽  
...  
Keyword(s):  
High Resolution ◽  
Terrain Analysis ◽  
Digital Terrain Analysis ◽  
Digital Terrain ◽  
Topography Data

scholarly journals Case-based knowledge formalization and reasoning method for digital terrain analysis – application to extracting drainage networks

2016 ◽  
Vol 20 (8) ◽  
pp. 3379-3392 ◽  
Author(s):  
Cheng-Zhi Qin ◽  
Xue-Wei Wu ◽  
Jing-Chao Jiang ◽  
A-Xing Zhu
Keyword(s):  
Domain Knowledge ◽  
Case Based Reasoning ◽  
Preliminary Evaluation ◽  
Terrain Analysis ◽  
Digital Terrain Analysis ◽  
Digital Terrain ◽  
Drainage Networks ◽  
Building Process ◽  
Context Knowledge ◽  
Case Based

Abstract. Application of digital terrain analysis (DTA), which is typically a modeling process involving workflow building, relies heavily on DTA domain knowledge of the match between the algorithm (and its parameter settings) and the application context (including the target task, the terrain in the study area, the DEM resolution, etc.), which is referred to as application-context knowledge. However, existing DTA-assisted tools often cannot use application-context knowledge because this type of DTA knowledge has not been formalized to be available for inference in these tools. This situation makes the DTA workflow-building process difficult for users, especially non-expert users. This paper proposes a case-based formalization for DTA application-context knowledge and a corresponding case-based reasoning method. A case in this context consists of a series of indices that formalize the DTA application-context knowledge and the corresponding similarity calculation methods for case-based reasoning. A preliminary experiment to determine the catchment area threshold for extracting drainage networks has been conducted to evaluate the performance of the proposed method. In the experiment, 124 cases of drainage network extraction (50 for evaluation and 74 for reasoning) were prepared from peer-reviewed journal articles. Preliminary evaluation shows that the proposed case-based method is a suitable way to use DTA application-context knowledge to achieve a marked reduction in the modeling burden for users.

Digital Terrain Analysis Based on DEM

2006 ◽  
Vol 1 (1) ◽  
pp. 54-58
Author(s):  
Huaxing Bi ◽  
Xiaoyin Li ◽  
Mengxia Guo ◽  
Xin Liu ◽  
Jun Li
Keyword(s):  
Terrain Analysis ◽  
Digital Terrain Analysis ◽  
Digital Terrain

scholarly journals Case-based formalization and reasoning method for knowledge in digital terrain analysis – Illustrated by determining the catchment area threshold for extracting drainage networks

2016 ◽  
Author(s):  
C.-Z. Qin ◽  
X.-W. Wu ◽  
J.-C. Jiang ◽  
A.-X. Zhu
Keyword(s):  
Catchment Area ◽  
Domain Knowledge ◽  
Case Based Reasoning ◽  
Preliminary Evaluation ◽  
Terrain Analysis ◽  
Digital Terrain Analysis ◽  
Digital Terrain ◽  
Drainage Networks ◽  
Context Knowledge ◽  
Case Based

Abstract. Application of digital terrain analysis (DTA), which is typically a modeling process involving workflow building, relies heavily on DTA domain knowledge of the match between the algorithm (and its parameter settings) and the application context (including the target task, the terrain in the study area, the DEM resolution, etc.), which is referred to as application-context knowledge. However, existing DTA-assisted tools often cannot use application-context knowledge because this type of DTA knowledge has not been formalized to be available for inference in these tools. This situation makes the DTA workflow-building process difficult for users, especially non-expert users. This paper proposes a case-based formalization for DTA application-context knowledge and a corresponding case-based reasoning method. A case in this context consists of a series of indices that formalize the DTA application-context knowledge and the corresponding similarity calculation methods for case-based reasoning. A preliminary experiment to determine the catchment area threshold for extracting drainage networks has been conducted to evaluate the performance of the proposed method. In the experiment, 124 cases of drainage network extraction (50 for evaluation and 74 for reasoning) were prepared from peer-reviewed journal articles. Preliminary evaluation results show that the proposed case-based method is a suitable way to use DTA application-context knowledge to achieve a marked reduction in the modeling burden for users.

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