Large-scale tree detection through UAV-based remote sensing in Indonesia: Wallacea case study

Ibnu F. Kurniawan, Adel Aneiba, Ambreen Hussain, Moad Idrissi, Iswan Dunggio, A. Taufiq Asyhari

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

4 Citations (Scopus)

Abstract

The Wallacea region of Sulawesi, Indonesia is renowned for its biodiversity and exceptional endemism. Over the last decade, the region is vulnerable to deforestation, degradation and illegal activities. Frequent monitoring in terms of tree counting provides useful information for various stakeholders such as forest management, government institutions, and environmental agencies. Existing monitoring methods include labour intensive manual observations and satellite imaging remote sensing technology. Satellite-based imagery is low resolution, infrequent, and sometimes include cloud cover. To overcome these drawbacks, this research utilises UAV-based high-resolution RGB images processed by machine learning algorithm to detect tree species, i.e., Sugarpalm, Clove, and Coconut. We compared many deep learning algorithms and found that YOLOv5 model is lightweight, easy to use, fast and accurate for tree species identification.

Original languageEnglish
Title of host publicationProceedings - 2022 8th International Conference on Information Management, ICIM 2022
EditorsAli Al-Haj, Snehasish Banerjee
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages110-115
Number of pages6
ISBN (Electronic)9781665451741
ISBN (Print)9781665451758
DOIs
Publication statusPublished - 2022
Externally publishedYes
EventInternational Conference on Information Management 2022 - Cambridge, United Kingdom
Duration: 25 Mar 202227 Mar 2022
Conference number: 8th
https://ieeexplore.ieee.org/xpl/conhome/9844831/proceeding (Proceedings)
http://www.icim.org/ (Website)

Conference

ConferenceInternational Conference on Information Management 2022
Abbreviated titleICIM 2022
Country/TerritoryUnited Kingdom
CityCambridge
Period25/03/2227/03/22
Internet address

Keywords

  • Deep learning application
  • Forest monitoring
  • Remote sensing
  • Wallacea region

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