The DOI matches its Crossref record; 160 of the 166 references it cites were found in a registry or an index.
Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis
Open accessAbstract
Availability of very high-resolution remote sensing images and advancement of deep learning methods have shifted the paradigm of image classification from pixel-based and object-based methods to deep learning-based semantic segmentation. This shift demands a structured analysis and revision of the current status on the research domain of deep learning-based semantic segmentation. The focus of this paper is on urban remote sensing images. We review and perform a meta-analysis to juxtapose recent papers in terms of research problems, data source, data preparation methods including pre-processing and augmentation techniques, training details on architectures, backbones, frameworks, optimizers, loss functions and other hyper-parameters and performance comparison. Our detailed review and meta-analysis show that deep learning not only outperforms traditional methods in terms of accuracy, but also addresses several challenges previously faced. Further, we provide future directions of research in this domain.
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“DL-based methods have the advantage of accurate detection and classification, but only if the training and test datasets are from the same or similar environment [42] such that they are not affected by contextual and spatial uncertainties [43].”
From Real-Time Vehicle Classification and Tracking Using a Transfer Learning-Improved Deep Learning Network (2022) · cites this paper as [42]
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Cites
2 retracted
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Cited by
Sorted by citations
A comprehensive review on deep learning based remote sensing image super-resolution methods
Wang, P., Bayram, B., & Sertel, E. (2022) · Earth-Science Reviews · doi:10.1016/j.earscirev.2022.104110 · Cited by 382
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Forest Fire Risk Prediction: A Spatial Deep Neural Network-Based Framework
Naderpour, M., Rizeei, H. M., & Ramezani, F. (2021) · Remote Sensing · doi:10.3390/rs13132513 · Cited by 159
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A review of remote sensing image segmentation by deep learning methods
Li, J., Cai, Y., Li, Q., Kou, M., & Zhang, T. (2024) · International Journal of Digital Earth · doi:10.1080/17538947.2024.2328827 · Cited by 151
Metadata
- Authors
- Neupane, B., Horanont, T., & Aryal, J.
- Affiliations
- Advanced Geospatial Technology Research Unit, Sirindhorn International Institute of Technology, 131 Moo 5, Tiwanon Road, Bangkadi, Mueang Pathumthani, Pathumthani 12000, Thailand
- School of Information, Computer, and Communication Technology (ICT), Sirindhorn International Institute of Technology, 131 Moo 5, Tiwanon Road, Bangkadi, Mueang Pathumthani, Pathumthani 12000, Thailand
- The University of Melbourne (AU)
- Corresponding author
- Horanont, Teerayut
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2021-02-23
- Volume
- 13
- Issue
- 4
- Pages
- 808
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W3129544296
- Cited by
- 247
- References
- 166
- Citations by year
- 2026: 31 · 2025: 57 · 2024: 60 · 2023: 55 · 2022: 36 · 2021: 7
- FWCI
- 17.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Remote Sensing (publishedVersion, cc-by)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Thammasat University (Thammasat University Research Fund under the TU Research Scholar, Contract No. 6/2562.)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Automated Road and Building Extraction
- Keywords
- semantic segmentation, urban characteristics, satellite imagery, very high resolution remote sensing, deep learning, data augmentation, data preprocessing, hyperparameters, loss functions, performance comparison
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