Multispectral Drone Data Analysis on Coastal Dunes

Britnie Gonzalez-Moodie, Shane Daiek, Jorge Lorenzo-Trueba, Aparna S. Varde

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We devise a method to study coastal dune vegetation based on drone data orthomosaic mapping and machine learning algorithms to distinguish objects with spatial and color accuracy from numerous high-quality drone multispectral images. It allows accurate surveying on the density of coastal dune vegetation and potentially individual species. We thus analyze big data to develop tools for coastal resiliency and sustainability.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
EditorsYixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5903-5905
Number of pages3
ISBN (Electronic)9781665439022
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States
Duration: 15 Dec 202118 Dec 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference2021 IEEE International Conference on Big Data, Big Data 2021
Country/TerritoryUnited States
CityVirtual, Online
Period15/12/2118/12/21

Keywords

  • ANN
  • Big Data
  • Climate Change
  • Drone Images
  • Environmental Science
  • Multispectral Data
  • Random Forests
  • SVM

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