eprintid: 10198065
rev_number: 14
eprint_status: archive
userid: 699
dir: disk0/10/19/80/65
datestamp: 2024-10-04 08:40:45
lastmod: 2025-03-10 17:34:53
status_changed: 2024-10-04 08:40:45
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Gong, Jie
creators_name: Burningham, Helene
title: Automated Mapping of Surface Sedimentary Features in Mixed  Sand-Gravel Tidal Inlets Using UAV, XGBoost and U-Net
ispublished: pub
divisions: UCL
divisions: B03
divisions: C03
divisions: F26
keywords: Tidal inlet, sedimentary features classification, UAV, XGBoost, U-Net
note: This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions.
abstract: Understanding the distribution of surface sedimentary features within tidal inlets is crucial for assessing their morphodynamic response to waves and tides. Sediment size significantly influences sediment transport and deposition dynamics. However, in mixed sand-gravel tidal inlets, classifying sediment distribution poses unique challenges. Previous research has relied on labour-intensive field sampling, while the rapid spatial changes in the surface features challenge traditional surveying methods and reduce the potential for frequent monitoring. Although satellite imagery offers regular observations, low resolutions hinder the accurate classification of detailed surface characteristics. This study integrates consumer-grade UAV technology with XGboost and U-Net (ResNet34) model to develop automated high-resolution mapping models for surface features in a mixed sand-gravel tidal inlet at the mouth of the Deben estuary, based on the RGB images. The results show that both XGBoost and U-Net have good performance and high potential to classify surface sediments and map these at the pixel level in mixed sand-gravel systems, with relatively high accuracy in the prediction of gravel, sand and vegetation cover. These combined methods demonstrate the potential for regular UAV monitoring of tidal inlets over short- and long-term scales, which can enhance our morphodynamic understanding and contribute to the coastal monitoring and management.
date: 2025-01-14
date_type: published
publisher: Coastal Education and Research Foundation
official_url: https://meridian.allenpress.com/jcr/article/113/SI/710/505248/Automated-Mapping-of-Surface-Sedimentary-Features
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2324662
doi: 10.2112/JCR-SI113-140.1
lyricists_name: Gong, Jie
lyricists_id: JGONG90
actors_name: Gong, Jie
actors_id: JGONG90
actors_role: owner
full_text_status: public
publication: Journal of Coastal Research
volume: SI
number: 113
pagerange: 710-714
citation:        Gong, Jie;    Burningham, Helene;      (2025)    Automated Mapping of Surface Sedimentary Features in Mixed Sand-Gravel Tidal Inlets Using UAV, XGBoost and U-Net.                   Journal of Coastal Research , SI  (113)   pp. 710-714.    10.2112/JCR-SI113-140.1 <https://doi.org/10.2112/JCR-SI113-140.1>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10198065/1/JCR%20paper_Gong_Burningham.pdf