Hedgerows Monitoring in Remote Sensing: A Comprehensive Review

被引:0
作者
Pirbasti, Mehran Alizadeh [1 ]
McArdle, Gavin [2 ]
Akbari, Vahid [3 ]
机构
[1] Univ Coll Dublin, SFI Ctr Res Training Machine Learning, Sch Comp Sci, Dublin 4, Ireland
[2] Univ Coll Dublin, Sch Comp Sci, Dublin 4, Ireland
[3] Univ Stirling, Div Comp Sci & Math, Stirling FK9 4LA, Scotland
来源
IEEE ACCESS | 2024年 / 12卷
基金
爱尔兰科学基金会;
关键词
Remote sensing; Urban areas; Optical sensors; Vegetation mapping; Vegetation; Monitoring; Green products; Biodiversity; Sensors; Deep learning; Synthetic aperture radar; Laser radar; Hedgerow; remote sensing; machine learning; deep learning; synthetic aperture radar; LiDAR; SYNTHETIC-APERTURE RADAR; OBJECT-BASED CLASSIFICATION; FOREST BIOMASS; AGRICULTURAL LANDSCAPES; RURAL LANDSCAPE; SEMANTIC SEGMENTATION; VEGETATION; IMAGES; EXTRACTION; MANAGEMENT;
D O I
10.1109/ACCESS.2024.3485512
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:156184 / 156207
页数:24
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