Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/36475
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Hedgerows Monitoring in Remote Sensing: A Comprehensive Review
Author(s): Pirbasti, Mehran Alizadeh
McArdle, Gavin
Akbari, Vahid
Contact Email: vahid.akbari@stir.ac.uk
Issue Date: 2024
Date Deposited: 8-Nov-2024
Citation: Pirbasti MA, McArdle G & Akbari V (2024) Hedgerows Monitoring in Remote Sensing: A Comprehensive Review. <i>IEEE Access</i>, 12, pp. 156184 - 156207. https://doi.org/10.1109/access.2024.3485512
Abstract: This comprehensive review delves into the importance of hedgerows in urban green spaces, emphasizing their significant role in sustainable development and providing ecological benefits. Accurate identification and characterization mapping of hedgerows are vital for effective land management, urban planning, and conservation efforts. The article explores the challenges associated with identifying hedgerows in urban environments and the complexities they present for automatic detection. It discusses the limitations of traditional methods and showcases the potential of advances in remote sensing technologies and artificial intelligence (AI) methods, such as deep learning algorithms. Results indicate that deep learning can generally achieve an accuracy of 75% for hedgerow identification. This review article sets out a vision for the future of hedgerow detection and monitoring.
DOI Link: 10.1109/access.2024.3485512
Rights: 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
Licence URL(s): http://creativecommons.org/licenses/by-nc-nd/4.0/

Files in This Item:
File Description SizeFormat 
Hedgerows_Monitoring_in_Remote_Sensing_A_Comprehensive_Review.pdfFulltext - Published Version4.08 MBAdobe PDFView/Open



This item is protected by original copyright



A file in this item is licensed under a Creative Commons License Creative Commons

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.