Cited by 247Remote-Sensing Image ClassificationAdvanced Neural Network ApplicationsAutomated Road and Building Extraction

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Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis

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Neupane, Bipul, Horanont, Teerayut, Aryal, Jagannath·2021·Remote Sensing

DOI 10.3390/rs13040808

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Abstract

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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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
ORCID: Neupane, Bipul
Horanont, Teerayut
Aryal, Jagannath
Venue: Remote Sensing
Publisher: MDPI AG
Published: 2021-02-23
Volume: 13
Issue: 4
Pages: 808
ISSN: 2072-4292 (ISSN-L)
DOI: 10.3390/rs13040808
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: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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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Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis

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