A study-based ranking of LiDAR data visualization schemes aided by georectified aerial images
Bibliographic Data
| ID | 12751143 |
|---|---|
| Authors | Suddhasheel Ghosh (Jawaharlal Nehru Medical College, corresponding author), Bharat Lohani (0000-0001-8589-192X, Indian Institute of Technology Kanpur), Neeraj Misra (0000-0002-8412-1904, Indian Institute of Technology Kanpur) |
| Year | 2014 |
| Volume | 41 |
| Issue | 2 |
| Pages | 138-150 |
| Publication date | 2014-01-20 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Cartography and Geographic Information Science (JOURNAL) |
| Journal identifiers | ISSN: 1523-0406 • E-ISSN: 1545-0465 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/15230406.2014.880071 |
| OpenAlex | W2027906823 |
| Language | EN |
| References cited | 18 |
Light Detection and Ranging (LiDAR) collects dense 3D topographic information in the form of points. LiDAR data can be displayed either through direct rendering of the point cloud or by generalizing features extracted through classification or segmentation. We are working in the domain of visualizing LiDAR data sets and have developed certain pipelines for visualization. These pipelines have been presented elsewhere. We present a technique for the evaluation of visualization schemes for LiDAR data, by conducting a visualization experience survey for 13 pre-processing and visualization schemes where 60 participants rated these schemes on a 10 point scale on a questionnaire. The paper establishes a ranking for the different visualization schemes described herein. Finally, this paper establishes that our heuristic-based algorithm (presented elsewhere) performs almost equal to a classification-based visualization pipeline made using professional software. We believe that the presented technique can be used to assess other geospatial visualization schemes
Computer vision · Data mining · Data visualization · Geography · Geospatial analysis · Lidar · Pipeline (software · Point cloud · Ranging · Remote sensing · Rendering (computer graphics · Segmentation · Visualization · 3D Modeling in Geospatial Applications · Computer Science · Geographic Information Systems Studies · Remote Sensing and LiDAR Applications
Use of Ranks in One-Criterion Variance Analysis
The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance
Do Data Characteristics Change According to the Number of Scale Points Used? An Experiment Using 5-Point, 7-Point and 10-Point Scales
A New Measure of Rank Correlation
Research Challenges in Geovisualization
| Citation velocity | historical |
|---|---|
| Highly cited | No |