Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 24, issue 9
Articles | Volume 24, issue 9
https://doi.org/10.5194/nhess-24-3075-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-24-3075-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 24, issue 9
Research article
 | 
13 Sep 2024
Research article |  | 13 Sep 2024

AscDAMs: advanced SLAM-based channel detection and mapping system

Tengfei Wang, Fucheng Lu, Jintao Qin, Taosheng Huang, Hui Kong, and Ping Shen

Viewed

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 428 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
380 34 14 428 9 8
  • HTML: 380
  • PDF: 34
  • XML: 14
  • Total: 428
  • BibTeX: 9
  • EndNote: 8
Views and downloads (calculated since 24 May 2024)
Cumulative views and downloads (calculated since 24 May 2024)

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 428 (including HTML, PDF, and XML) Thereof 395 with geography defined and 33 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 22 Nov 2024
Download
Short summary
Harsh environments limit the use of drone, satellite, and simultaneous localization and mapping technology to obtain precise channel morphology data. We propose AscDAMs, which includes a deviation correction algorithm to reduce errors, a point cloud smoothing algorithm to diminish noise, and a cross-section extraction algorithm to quantitatively assess the morphology data. AscDAMs solves the problems and provides researchers with more reliable channel morphology data for further analysis.
Read more
Altmetrics
Final-revised paper
Preprint