A hybrid level-of-detail representation for large-scale urban scenes rendering

2014 ◽  
Vol 25 (3-4) ◽  
pp. 243-253 ◽  
Author(s):  
Shengchuan Zhou ◽  
Innfarn Yoo ◽  
Bedrich Benes ◽  
Ge Chen
Keyword(s):  
Large Scale ◽  
Level Of Detail ◽  
Urban Scenes ◽  
Detail Representation

A hybrid level-of-detail modeling and rendering approach for the visualization of large-scale urban scenes

2017 ◽  
pp. 305-310
Author(s):  
Zhihua Zhang ◽  
Haiyin Wang ◽  
Zhenbiao Hu ◽  
Jiuyan Zhang ◽  
Shengchuan Zhou
Keyword(s):  
Large Scale ◽  
Level Of Detail ◽  
Urban Scenes

scholarly journals Evaluating Rockfall Risk: Some Critical Aspects

Geosciences ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 98 ◽  
Author(s):  
Claudio Scavia ◽  
Monica Barbero ◽  
Marta Castelli ◽  
Maddalena Marchelli ◽  
Daniele Peila ◽  
...  
Keyword(s):  
Large Scale ◽  
Risk Estimation ◽  
Risk Process ◽  
Level Of Detail ◽  
Risk Level ◽  
Multi Scale ◽  
Mountain Environments ◽  
Open Questions ◽  
Hazard And Risk ◽  
Historical Heritage

Rockfalls evolve rapidly and unpredictably in mountain environments and can cause considerable losses to human societies, structures, economical activities, and also natural and historical heritage. Rockfall risk analyses are complex and multi-scale processes involving several disciplines and techniques. This complexity is due to the main features of rockfall phenomena, which are extremely variable over space and time. Today, a considerable number of methods exists for protecting land, as well as assessing and managing the risk level. These methodologies are often very different from each other, depending on the data required, the purposes of the analysis, and the reference scale adopted, i.e., the analysis level of detail. Nevertheless, several questions still remain open with reference to each phase of the hazard and risk process. This paper is devoted to a general overview of existing risk estimation methodologies and a critical analysis of some open questions with the aim of highlighting possible further research topics. A typical risk assessment framework is exemplified by analyzing a real case study. Each step of the process is treated at both the detailed and the large scale in order to highlight the main characteristics of each level of detail.

On the Role of Representations for Reasoning in Large-Scale Urban Scenes

2017 ◽  
Author(s):  
Randi Cabezas ◽  
Maros Blaha ◽  
Sue Zheng ◽  
Guy Rosman ◽  
Konrad Schindler ◽  
...  
Keyword(s):  
Large Scale ◽  
Urban Scenes

scholarly journals BRLO-Tree: A Data Structure Used for 3D GIS Dynamic Scene Rendering

2015 ◽  
Vol 15 (4) ◽  
pp. 124-137 ◽  
Author(s):  
Wenju Wang ◽  
Zhang Xuan ◽  
Liujie Sun ◽  
Zhongmin Jiang ◽  
Jingjing Shang
Keyword(s):  
Data Structure ◽  
Large Scale ◽  
Three Dimensional ◽  
Level Of Detail ◽  
Dynamic Scene ◽  
3D Gis ◽  
Dynamic Scenes ◽  
Complex Dynamic ◽  
3D Objects ◽  
System A

Abstract BRLO-Tree (Block-R-Tree-Loose-Octree) is presented in this paper based on the R-Tree and Loose-Octree. The aim of the structure is to visualize the large scale and complex dynamic scenes in a 3D (three-dimensional) GIS (Geographic Information System). A new method of clustering rectangles to construct R-tree based on an improved K-means algorithm is put forward. Landform in 3D GIS is organized by R-Tree. The block is used as the basic rendering unit. The 3D objects of each block are respectively organized by a Loose-Octree. A series of techniques, based on this data structure, such as LOD (Level of Detail), relief impostors are integrated. The results of the tests show that BRLO-Tree cannot only support the large scale 3D GIS scene exhibition with wandering and fighting, but it can also efficiently manage the models in a dynamic scene. At the same time, a set of integrated techniques based on BRLO-Tree can make the rendering pictures more fluence and the rendering time vastly improved.

scholarly journals LARGE-SCALE CARTOGRAPHIC REPRESENTATION OF RELIEF FEATURES FROM SANDYZATION PROCESS

2020 ◽  
Vol 26 (4) ◽  
Author(s):  
Bibiana Salvador Cabral da Costa ◽  
Claudia Robbi Sluter ◽  
Andrea Lopes Iescheck ◽  
Éder Luís da Silva Rodrigues
Keyword(s):  
Large Scale ◽  
Level Of Detail ◽  
Topographic Maps ◽  
Surface Model ◽  
Digital Surface Model ◽  
Concentrated Flow ◽  
Contour Lines ◽  
Remotely Piloted Aircraft ◽  
Data Survey ◽  
Geomorphological Features

Abstract: In topographic maps, contour lines and elevation points usually represent the variation of height and slope. Contour lines interval defines the level of detail for relief representation. Geomorphological features we can identify on maps are related to contour lines generalization. In this study, we aim to define the necessary level of detail for the cartographic representation of relief features from the sandyzation process. The methodology comprises: defining the relief features associated with sandyzation at the study area by literature review; describing the aspects of data survey using Remotely Piloted Aircraft (RPA) to generate the orthophoto mosaic and the Digital Surface Model (DSM); and using the DSM to extract contour lines at different scales. We defined eight relief features (denudational landform, rill, ravine, micro-residual hill, dune, depositional fan, concentrated flow, and gully) for contour cartographic representation at 1:5,000, 1:1,000, 1:500, 1:200, and 1:100 scales. The results show the scales in which the relief features have their geomorphological characteristics better represented by contours lines. Since there is no reference for suitable scales for the cartographic representation of landforms related to the sandyzation process, this study can contribute to geomorphological researches in areas where this process occurs.

scholarly journals An energy minimization approach to automated extraction of regular building footprints from airborne LiDAR data

2014 ◽  
Vol II-3 ◽  
pp. 65-72 ◽  
Author(s):  
Y. He ◽  
C. Zhang ◽  
C. S. Fraser
Keyword(s):  
Energy Function ◽  
Energy Minimization ◽  
Large Scale ◽  
Spatial Information ◽  
Structural Complexity ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Urban Scenes ◽  
Irregular Point ◽  
Airborne Lidar Data

This paper presents an automated approach to the extraction of building footprints from airborne LiDAR data based on energy minimization. Automated 3D building reconstruction in complex urban scenes has been a long-standing challenge in photogrammetry and computer vision. Building footprints constitute a fundamental component of a 3D building model and they are useful for a variety of applications. Airborne LiDAR provides large-scale elevation representation of urban scene and as such is an important data source for object reconstruction in spatial information systems. However, LiDAR points on building edges often exhibit a jagged pattern, partially due to either occlusion from neighbouring objects, such as overhanging trees, or to the nature of the data itself, including unavoidable noise and irregular point distributions. The explicit 3D reconstruction may thus result in irregular or incomplete building polygons. In the presented work, a vertex-driven Douglas-Peucker method is developed to generate polygonal hypotheses from points forming initial building outlines. The energy function is adopted to examine and evaluate each hypothesis and the optimal polygon is determined through energy minimization. The energy minimization also plays a key role in bridging gaps, where the building outlines are ambiguous due to insufficient LiDAR points. In formulating the energy function, hard constraints such as parallelism and perpendicularity of building edges are imposed, and local and global adjustments are applied. The developed approach has been extensively tested and evaluated on datasets with varying point cloud density over different terrain types. Results are presented and analysed. The successful reconstruction of building footprints, of varying structural complexity, along with a quantitative assessment employing accurate reference data, demonstrate the practical potential of the proposed approach.

scholarly journals Discovering place-informative scenes and objects using social media photos

2019 ◽  
Vol 6 (3) ◽  
pp. 181375 ◽  
Author(s):  
Fan Zhang ◽  
Bolei Zhou ◽  
Carlo Ratti ◽  
Yu Liu
Keyword(s):  
Social Media ◽  
Urban Design ◽  
Architectural Design ◽  
Visual Cues ◽  
Large Scale ◽  
Urban Scenes ◽  
Visual Knowledge ◽  
Historical Architecture ◽  
Police Cars ◽  
The City

Understanding the visual discrepancy and heterogeneity of different places is of great interest to architectural design, urban design and tourism planning. However, previous studies have been limited by the lack of adequate data and efficient methods to quantify the visual aspects of a place. This work proposes a data-driven framework to explore the place-informative scenes and objects by employing deep convolutional neural network to learn and measure the visual knowledge of place appearance automatically from a massive dataset of photos and imagery. Based on the proposed framework, we compare the visual similarity and visual distinctiveness of 18 cities worldwide using millions of geo-tagged photos obtained from social media. As a result, we identify the visual cues of each city that distinguish that city from others: other than landmarks, a large number of historical architecture, religious sites, unique urban scenes, along with some unusual natural landscapes have been identified as the most place-informative elements. In terms of the city-informative objects, taking vehicles as an example, we find that the taxis, police cars and ambulances are the most place-informative objects. The results of this work are inspiring for various fields—providing insights on what large-scale geo-tagged data can achieve in understanding place formalization and urban design.

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