Airborne LiDAR-assisted deep learning methodology for riparian land cover classification using aerial photographs and its application for flood modelling

Keisuke Yoshida, Shijun Pan, Junichi Taniguchi, Satoshi Nishiyama, Takashi Kojima, Md Touhidul Islam

Research output: Contribution to journalArticlepeer-review

Abstract

In response to challenges in land cover classification (LCC), many researchers have experimented recently with classification methods based on artificial intelligence techniques. For LCC mapping of the vegetated Asahi River in Japan, the current study uses deep learning (DL)-based DeepLabV3þ module for image segmentation of aerial photographs. We modified the existing model by concatenating data on its resultant output port to access the airborne laser bathymetry (ALB) dataset, including voxel-based laser points and vegetation height (i.e. digital surface model data minus digital terrain model data). Findings revealed that the modified approach improved the accuracy of LCC greatly compared to our earlier unsupervised ALB-based method, with 25 and 35% improvement, respectively, in overall accuracy and the macro F1-score for November 2017 dataset (no-leaf condition). Finally, by estimating flow-resistance parameters in flood modelling using LCC mapping-derived data, we conclude that the upgraded DL methodology produces better fit between numerically analyzed and observed peak water levels.

Original languageEnglish
Pages (from-to)179-201
Number of pages23
JournalJournal of Hydroinformatics
Volume24
Issue number1
DOIs
Publication statusPublished - Jan 1 2022

Keywords

  • Airborne laser bathymetry
  • Deep learning
  • Flow-resistance parameterization
  • Riparian land cover classification
  • Semantic segmentation

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Water Science and Technology
  • Geotechnical Engineering and Engineering Geology
  • Atmospheric Science

Fingerprint

Dive into the research topics of 'Airborne LiDAR-assisted deep learning methodology for riparian land cover classification using aerial photographs and its application for flood modelling'. Together they form a unique fingerprint.

Cite this