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Open AccessReview

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

1
Advanced Geospatial Technology Research Unit, Sirindhorn International Institute of Technology, 131 Moo 5, Tiwanon Road, Bangkadi, Mueang Pathumthani, Pathumthani 12000, Thailand
2
School of Information, Computer, and Communication Technology (ICT), Sirindhorn International Institute of Technology, 131 Moo 5, Tiwanon Road, Bangkadi, Mueang Pathumthani, Pathumthani 12000, Thailand
3
Department of Infrastructure Engineering, Faculty of Engineering and IT, The University of Melbourne, Melbourne, VIC 3010, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Guoqing Li
Remote Sens. 2021, 13(4), 808; https://doi.org/10.3390/rs13040808
Received: 18 January 2021 / Revised: 15 February 2021 / Accepted: 18 February 2021 / Published: 23 February 2021
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. View Full-Text
Keywords: deep learning; remote sensing; review; semantic segmentation; urban image classification deep learning; remote sensing; review; semantic segmentation; urban image classification
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MDPI and ACS Style

Neupane, B.; Horanont, T.; Aryal, J. Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis. Remote Sens. 2021, 13, 808. https://doi.org/10.3390/rs13040808

AMA Style

Neupane B, Horanont T, Aryal J. Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis. Remote Sensing. 2021; 13(4):808. https://doi.org/10.3390/rs13040808

Chicago/Turabian Style

Neupane, Bipul; Horanont, Teerayut; Aryal, Jagannath. 2021. "Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis" Remote Sens. 13, no. 4: 808. https://doi.org/10.3390/rs13040808

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