Name
Title | ||
|---|---|---|
Tree Annotations in LiDAR Data Using Point Densities and Convolutional Neural Networks. |
Abstract | ||
|---|---|---|
LiDAR provides highly accurate 3-D point clouds. However, data need to be manually labeled in order to provide subsequent useful information. Manual annotation of such data is time-consuming, tedious, and error prone, and hence, in this article, we present three automatic methods for annotating trees in LiDAR data. The first method requires high-density point clouds and uses certain LiDAR data att... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TGRS.2019.2942201 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Vegetation,Laser radar,Three-dimensional displays,Urban areas,Forestry,Training,Feature extraction | Computer vision,Convolutional neural network,Artificial intelligence,Deep learning,Lidar data,Mathematics | Journal |
Volume | Issue | ISSN |
58 | 2 | 0196-2892 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ananya Gupta | 1 | 5 | 2.91 |
| Jonathan Byrne | 2 | 0 | 0.34 |
| David Moloney | 3 | 12 | 7.69 |
| Simon Watson | 4 | 0 | 3.04 |
| Hujun Yin | 5 | 1577 | 149.88 |