Autonomous palm tree detection from remote sensing images-UAE dataset

Mina Al-Saad, Nour Aburaed, Saeed Al Mansoori, Hussain Al Ahmad

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

5 Citations (Scopus)
30 Downloads (Pure)

Abstract

Autonomous detection and counting of palm trees is a research field of interest to various countries around the world, including the UAE. Automating this task saves effort and resources by minimizing human intervention and reducing potential errors in counting. This paper introduces a new High Resolution (HR) remote sensing dataset for autonomous detection of palm trees in the UAE. The dataset is collected using Unmanned Aerial Vehicles (UAV), and it is labeled properly in PASCAL VOC and YOLO formats after preprocessing and visually inspecting its quality. A comparative evaluation between Faster-RCNN and YOLOv4 networks is then conducted to observe the usability of the dataset in addition to the strengths and weaknesses of each network. The dataset is publicly available at https://github.com/Nour093/Palm-Tree-Dataset.

Original languageEnglish
Title of host publicationIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Place of PublicationPiscataway, NJ
Pages2191-2194
Number of pages4
ISBN (Electronic)9781665427920
DOIs
Publication statusPublished - 28 Sept 2022
Externally publishedYes
Event2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia
Duration: 17 Jul 202222 Jul 2022

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2022-July

Conference

Conference2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Country/TerritoryMalaysia
CityKuala Lumpur
Period17/07/2222/07/22

Keywords

  • convolutional neural networks (CNN)
  • FRCNN
  • object detection
  • remote sensing
  • YOLOv4

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