scholarly journals A Linear Feature-Based Approach for the Registration of Unmanned Aerial Vehicle Remotely-Sensed Images and Airborne LiDAR Data

2016 ◽  
Vol 8 (2) ◽  
pp. 82 ◽  
Author(s):  
Shijie Liu ◽  
Xiaohua Tong ◽  
Jie Chen ◽  
Xiangfeng Liu ◽  
Wenzheng Sun ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Remotely Sensed ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Linear Feature ◽  
Aerial Vehicle ◽  
Feature Based ◽  
Remotely Sensed Images ◽  
Airborne Lidar Data

scholarly journals Automated mapping of relict patterned ground: An approach to evaluate morphologically subdued landforms using unmanned-aerial-vehicle and structure-from-motion technologies

2018 ◽  
Vol 43 (2) ◽  
pp. 174-192 ◽  
Author(s):  
AE Mather ◽  
RM Fyfe ◽  
CC Clason ◽  
M Stokes ◽  
S Mills ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Structure From Motion ◽  
Test Site ◽  
Image Resolution ◽  
Airborne Lidar ◽  
Patterned Ground ◽  
Automated Mapping ◽  
Aerial Vehicle ◽  
Modern Landscape ◽  
Airborne Lidar Data

Relict landforms provide a wealth of information on the evolution of the modern landscape and climate change in the past. To improve understanding of the origin and development of these landforms we need better spatial measurements across a variety of scales. This can be challenging using conventional surveying techniques due to difficulties in landform recognition on the ground (e.g. weak visual/topographic expression) and spatially variable areas of interest. Here we explore the appropriateness of existing remote sensing datasets (aerial LiDAR and aerial photography) and newly acquired unmanned aerial vehicle (UAV) imagery of a test site on the upland of Dartmoor in SW England (Leeden Tor) for the recognition and automated mapping of relict patterned ground composed of stripes and polygons. We find that the recognition of these landforms is greatly enhanced by automated mapping using spectral two-dimensional imagery. Image resolution is important, with the recognition of elements (boulders) of <1 m maximised from the highest resolution imagery (UAV red-green-blue (RGB)) and recognition of landforms (10–100 m scale) maximised on coarser resolution aerial imagery. Topographic metrics of these low relief (0.5 m) landforms are best extracted from structure-from-motion (SfM) processed UAV true-colour imagery, and in this context the airborne LiDAR data proved less effective. Integrating automated mapping using spectral attributes and SfM-derived digital surface models from UAV RGB imagery provides a powerful tool for rapid reconnaissance of field sites to facilitate the extraction of meaningful topographic and spatial metrics that can inform on the origin of relict landform features. Care should be given to match the scale of features under consideration to the appropriate scale of datasets available.

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space

2021 ◽  
Author(s):  
Renato César dos Santos ◽  
Mauricio Galo ◽  
André Caceres Carrilho ◽  
Guilherme Gomes Pessoa
Keyword(s):  
Urban Space ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Multi Temporal ◽  
Airborne Lidar Data

scholarly journals A Comparative Study about Data Structures Used for Efficient Management of Voxelised Full-Waveform Airborne LiDAR Data during 3D Polygonal Model Creation

2021 ◽  
Vol 13 (4) ◽  
pp. 559
Author(s):  
Milto Miltiadou ◽  
Neill D. F. Campbell ◽  
Darren Cosker ◽  
Michael G. Grant
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Airborne Lidar ◽  
Memory Allocation ◽  
Lidar Data ◽  
Full Waveform ◽  
Waveform Lidar ◽  
Airborne Lidar Data ◽  
Full Waveform Lidar ◽  
Polygonal Model

In this paper, we investigate the performance of six data structures for managing voxelised full-waveform airborne LiDAR data during 3D polygonal model creation. While full-waveform LiDAR data has been available for over a decade, extraction of peak points is the most widely used approach of interpreting them. The increased information stored within the waveform data makes interpretation and handling difficult. It is, therefore, important to research which data structures are more appropriate for storing and interpreting the data. In this paper, we investigate the performance of six data structures while voxelising and interpreting full-waveform LiDAR data for 3D polygonal model creation. The data structures are tested in terms of time efficiency and memory consumption during run-time and are the following: (1) 1D-Array that guarantees coherent memory allocation, (2) Voxel Hashing, which uses a hash table for storing the intensity values (3) Octree (4) Integral Volumes that allows finding the sum of any cuboid area in constant time, (5) Octree Max/Min, which is an upgraded octree and (6) Integral Octree, which is proposed here and it is an attempt to combine the benefits of octrees and Integral Volumes. In this paper, it is shown that Integral Volumes is the more time efficient data structure but it requires the most memory allocation. Furthermore, 1D-Array and Integral Volumes require the allocation of coherent space in memory including the empty voxels, while Voxel Hashing and the octree related data structures do not require to allocate memory for empty voxels. These data structures, therefore, and as shown in the test conducted, allocate less memory. To sum up, there is a need to investigate how the LiDAR data are stored in memory. Each tested data structure has different benefits and downsides; therefore, each application should be examined individually.

scholarly journals Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

2017 ◽  
Vol 9 (8) ◽  
pp. 771 ◽  
Author(s):  
Yanjun Wang ◽  
Qi Chen ◽  
Lin Liu ◽  
Dunyong Zheng ◽  
Chaokui Li ◽  
...  
Keyword(s):  
Urban Areas ◽  
Supervised Classification ◽  
Airborne Lidar ◽  
Power Lines ◽  
Lidar Data ◽  
Airborne Lidar Data
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