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Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

dc.contributor.authorMohamed Barakat A. Gibril
dc.contributor.authorHelmi Zulhaidi Mohd Shafri
dc.contributor.authorAbdallah Shanableh
dc.contributor.authorRami Al-Ruzouq
dc.contributor.authorAimrun Wayayok
dc.contributor.authorShaiful Jahari Hashim
dc.contributor.otherDepartment of Civil Engineering and Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
dc.contributor.otherDepartment of Civil Engineering and Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
dc.contributor.otherDepartment of Civil and Environmental Engineering, Faculty of Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates
dc.contributor.otherDepartment of Civil and Environmental Engineering, Faculty of Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates
dc.contributor.otherDepartment of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
dc.contributor.otherDepartment of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
dc.date.accessioned2025-10-09T04:53:10Z
dc.date.available2025-10-09T04:53:10Z
dc.date.issued01-07-2021
dc.identifier.urihttps://www.mdpi.com/2072-4292/13/14/2787
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/40821
dc.description.abstractLarge-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.
dc.language.isoEN
dc.publisherMDPI AG
dc.subject.lccScience
dc.titleDeep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images
dc.typeArticle
dc.description.keywordsdate palm trees
dc.description.keywordstree species classification
dc.description.keywordssemantic segmentation
dc.description.keywordsfully convolutional neural networks
dc.description.keywordsunmanned aerial vehicle (UAV)
dc.description.doi10.3390/rs13142787
dc.title.journalRemote Sensing
dc.identifier.e-issn2072-4292
dc.identifier.oaioai:doaj.org/journal:5a46f72b12d2467c9d0ba3d88b972c6e
dc.journal.infoVolume 13, Issue 14


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