Autonomous palm tree detection from remote sensing images-UAE dataset

Al-Saad, Mina and Aburaed, Nour and Mansoori, Saeed Al and Ahmad, Hussain Al; (2022) Autonomous palm tree detection from remote sensing images-UAE dataset. In: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. International Geoscience and Remote Sensing Symposium (IGARSS) . Institute of Electrical and Electronics Engineers Inc., MYS, pp. 2191-2194. ISBN 9781665427920 (https://doi.org/10.1109/IGARSS46834.2022.9884126)

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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.

ORCID iDs

Al-Saad, Mina, Aburaed, Nour ORCID logoORCID: https://orcid.org/0000-0002-5906-0249, Mansoori, Saeed Al and Ahmad, Hussain Al;