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

Neupane, Bipul; Horanont, Teerayut; Aryal, Jagannath

doi:10.3390/rs13040808

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

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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DL-based methods have accurate detection and classification only if the training and test datasets are from the same or similar environment.
“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]

2 retracted

TEMPORARY REMOVAL: Aerial imagery for roof segmentation: A large-scale dataset towards automatic mapping of buildings
Chen, Q., Wang, L., Wu, Y., Wu, G., Guo, Z., & Waslander, S. L. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.11.011 · Cited by 137 · see citing works
Retracted
RETRACTED ARTICLE: Wiener filter based deep convolutional network approach for classification of satellite images
Poomani, M., Sutha, J., & Soundar, K. R. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02410-3 · Cited by 2 · see citing works
Retracted
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works
Untitled work
(2014) · Cited by 33,419 · see citing works
A survey on transfer learning
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392 · see citing works
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054 · see citing works
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631 · see citing works
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599 · see citing works
Pyramid Scene Parsing Network
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.660 · Cited by 16,438 · see citing works
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389824 · Cited by 11,971 · see citing works
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Susstrunk, S. E. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.120 · Cited by 9,232 · see citing works
A survey of transfer learning
Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016) · Journal of Big Data · doi:10.1186/s40537-016-0043-6 · Cited by 6,260 · see citing works
Efficient Graph-Based Image Segmentation
Felzenszwalb, P. F., & Huttenlocher, D. P. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000022288.19776.77 · Cited by 6,205 · see citing works
Large-Scale Machine Learning with Stochastic Gradient Descent
Bottou, L. (2010) · Proceedings of COMPSTAT'2010 · doi:10.1007/978-3-7908-2604-3_16 · Cited by 5,795 · see citing works
Using Deep Learning for Image-Based Plant Disease Detection
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.01419 · Cited by 5,111 · see citing works
Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services
Drusch, M., Bello, U. D., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., et al. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.11.026 · Cited by 4,906 · see citing works
Convolutional networks for images, speech, and time series
LeCun, Y., & Bengio, Y. (1998) · HAL · doi:10.5555/303568.303704 · Cited by 4,637 · see citing works
Learning Deconvolution Network for Semantic Segmentation
Noh, H., Hong, S., & Han, B. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.178 · Cited by 4,422 · see citing works
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · see citing works
A survey of image classification methods and techniques for improving classification performance
Lu, D., & Weng, Q. (2007) · International Journal of Remote Sensing · doi:10.1080/01431160600746456 · Cited by 3,424 · see citing works
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762307 · Cited by 3,400 · see citing works
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation
Lin, G., Milan, A., Shen, C., & Reid, I. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.549 · Cited by 3,287 · cited 1× · see citing works
“RefineNet [122] is similar to U-Net, but introduces several residual convolutional blocks inside the encoder and decoder.”
Method
A Survey on Deep Transfer Learning
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01424-7_27 · Cited by 3,165 · see citing works
Learning Hierarchical Features for Scene Labeling
Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.231 · Cited by 2,620 · see citing works
Deep learning in remote sensing applications: A meta-analysis and review
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456 · see citing works
Conditional Random Fields as Recurrent Neural Networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., & Torr, P. H. S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.179 · Cited by 2,386 · cited 1× · see citing works
“[82] with Gaussian pairwise potentials as recurrent neural fields (RNNs) to train an end-to-end CRF-as-RNN with unary energy of CRF.”
Method
ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data
Diakogiannis, F. I., Waldner, F., Caccetta, P. A., & Wu, C. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.01.013 · Cited by 2,210 · see citing works
Deep Boltzmann machines
Salakhutdinov, R., & Hinton, G. E. (2009) · Cited by 1,890 · see citing works
Transfer Learning
Torrey, L. A., & SHAVLIK, J. W. (2010) · IGI Global eBooks · doi:10.4018/978-1-60566-766-9.ch011 · Cited by 1,887 · see citing works
Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
Kussul, N., Лавренюк, М. С., Skakun, S. V., & Шелестов, А. Ю. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2681128 · Cited by 1,799 · see citing works
Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery
Myint, S. W., Gober, P. A., Brazel, A. J., Grossman‐Clarke, S., & Weng, Q. (2011) · Remote Sensing of Environment · doi:10.1016/j.rse.2010.12.017 · Cited by 1,242 · see citing works
The next Landsat satellite: The Landsat Data Continuity Mission
Irons, J. R., Dwyer, J. L., & Barsi, J. A. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.08.026 · Cited by 971 · see citing works
Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners
Bisong, E. (2019) · doi:10.1007/978-1-4842-4470-8 · Cited by 895 · see citing works
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S. R., Dirk Tiede, A., & Aryal, J. (2019) · Remote Sensing · doi:10.3390/rs11020196 · Cited by 891 · see citing works
Self-citation
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
Lin, G., Shen, C., van den Hengel, A., & Reid, I. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.348 · Cited by 862 · see citing works
Learning to Refine Object Segments
Pinheiro, P. O., Lin, T.-Y., Collobert, R., & Dollár, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46448-0_5 · Cited by 768 · see citing works
Learning to Segment Object CandidatesPreprint
Pinheiro, P. H. O., Collobert, R., & Dollár, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.06204 · Cited by 710 · see citing works
Classification With an Edge: Improving Semantic Image Segmentation with Boundary DetectionPreprint
Marmanis, D., Schindler, K., Wegner, J. D., Galliani, S., Datcu, M. P., & Stilla, U. (2016) · arXiv (Cornell University) · doi:10.1016/j.isprsjprs.2017.11.009 · Cited by 681 · see citing works
Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks
Audebert, N., Saux, B. L., & Lefèvre, S. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.11.011 · Cited by 678 · see citing works
Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks
Kampffmeyer, M., Salberg, A.-B., & Jenssen, R. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.90 · Cited by 618 · see citing works
Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning
Kemker, R., Salvaggio, C., & Kanan, C. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.04.014 · Cited by 558 · see citing works
Object-based classification of remote sensing data for change detection
Walter, V. (2003) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2003.09.007 · Cited by 544 · see citing works
Deep learning for remote sensing image classification: A survey
Li, Y., Zhang, H., Xue, X., Jiang, Y., & Shen, Q. (2018) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1264 · Cited by 486 · see citing works
Fuzzy classification of remote sensing images
Wang, F. (1990) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.46698 · Cited by 472 · see citing works
Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks
Volpi, M., & Tuia, D. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2616585 · Cited by 460 · see citing works
The use of backpropagating artificial neural networks in land cover classification
Kavzoğlu, T., & Mather, P. M. (2003) · International Journal of Remote Sensing · doi:10.1080/0143116031000114851 · Cited by 459 · see citing works
Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil
POWELL, R., Roberts, D. L., Dennison, P. E., & Hess, L. (2006) · Remote Sensing of Environment · doi:10.1016/j.rse.2006.09.005 · Cited by 433 · see citing works
A Deep Learning-based Approach for Banana Leaf Diseases Classification
Amara, J., Bouaziz, B., & Algergawy, A. (2017) · BTW Workshops · Cited by 432 · see citing works
Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks
Audebert, N., Saux, B. L., & Lefèvre, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54181-5_12 · Cited by 386 · see citing works
DeepUNet: A Deep Fully Convolutional Network for Pixel-Level Sea-Land Segmentation
Li, R., Liu, W., Yang, L., Sun, S., Hu, W., Zhang, F., & Li, W. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2833382 · Cited by 382 · see citing works
FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY
Häckel, T., Wegner, J. D., & Schindler, K. (2016) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iii-3-177-2016 · Cited by 373 · see citing works
Artificial neural networks for land-cover classification and mapping
Civco, D. L. (1993) · International Journal of Geographical Information Systems · doi:10.1080/02693799308901949 · Cited by 368 · see citing works
Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks
Wurm, M., Stark, T., Zhu, X. X., Weigand, M., & Taubenböck, H. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.02.006 · Cited by 363 · see citing works
Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network
Fu, G., Liu, C., Zhou, R. F., Sun, T., & Zhang, Q. (2017) · Remote Sensing · doi:10.3390/rs9050498 · Cited by 345 · see citing works
Learning Aerial Image Segmentation From Online Maps
Kaiser, P., Wegner, J. D., Lucchi, A., Jaggi, M., Hofmann, T., & Schindler, K. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2719738 · Cited by 336 · see citing works
Urban Land-Cover Change Detection through Sub-Pixel Imperviousness Mapping Using Remotely Sensed Data
Yang, L., Xian, G., Klaver, J. M., & Deal, B. M. (2003) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.69.9.1003 · Cited by 327 · see citing works
Semantic labeling in very high resolution images via a self-cascaded convolutional neural network
Liu, Y., Fan, B., Wang, L., Bai, J., Xiang, S., & Pan, C. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.12.007 · Cited by 290 · see citing works
Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks
Bischke, B., Helber, P., Folz, J., Borth, D., & Dengel, A. R. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803050 · Cited by 287 · see citing works
Semisupervised hyperspectral image classification based on Generative Adversarial Networks
Zhan, Y., Hu, D., Wang, Y., & Yu, X. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2780890 · Cited by 283 · see citing works
Decoupled Deep Neural Network for Semi-supervised Semantic SegmentationPreprint
Hong, S., Noh, H., & Han, B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.04924 · Cited by 283 · see citing works
Multispectral Image Analysis Using the Object-Oriented Paradigm
Navulur, K. (2006) · CRC Press eBooks · doi:10.1201/9781420043075 · Cited by 276 · see citing works
Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks
Hu, X., & Weng, Q. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.05.014 · Cited by 274 · see citing works
SEMANTIC SEGMENTATION OF AERIAL IMAGES WITH AN ENSEMBLE OF CNNS
Marmanis, D., Wegner, J. D., Galliani, S., Schindler, K., Datcu, M. P., & Stilla, U. (2016) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iii-3-473-2016 · Cited by 272 · see citing works
Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images
Audebert, N., Saux, B. L., & Lefèvre, S. (2017) · Remote Sensing · doi:10.3390/rs9040368 · Cited by 264 · see citing works
Semantic Segmentation-Based Building Footprint Extraction Using Very High-Resolution Satellite Images and Multi-Source GIS Data
Li, W., He, C., Fang, J., Zheng, J., Fu, H., & Yu, L. (2019) · Remote Sensing · doi:10.3390/rs11040403 · Cited by 251 · see citing works
Effective semantic pixel labelling with convolutional networks and Conditional Random Fields
Paisitkriangkrai, S., Sherrah, J., Janney, P., & van den Hengel, A. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301381 · Cited by 240 · see citing works
Unsupervised Domain Adaptation Using Generative Adversarial Networks for Semantic Segmentation of Aerial Images
Benjdira, B., Bazi, Y., Koubâa, A., & Ouni, K. (2019) · Remote Sensing · doi:10.3390/rs11111369 · Cited by 236 · see citing works
MARTA GANs: unsupervised representation learning for remote sensing image classification
Lin, D., Fu, K., Wang, Y., Xu, G., & Sun, X. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2752750 · Cited by 230 · see citing works
Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network
Yi, Y., Zhang, Z., Zhang, W., Zhang, C., Li, W., & Zhao, T. (2019) · Remote Sensing · doi:10.3390/rs11151774 · Cited by 229 · see citing works
Deep Learning Segmentation and Classification for Urban Village Using a Worldview Satellite Image Based on U-Net
Pan, Z., Xu, J., Guo, Y., Hu, Y., & Wang, G. (2020) · Remote Sensing · doi:10.3390/rs12101574 · Cited by 225 · see citing works
Multiple Object Extraction from Aerial Imagery with Convolutional Neural Networks
Saito, S., Yamashita, T., & Aoki, Y. (2016) · IRIACV · doi:10.2352/issn.2470-1173.2016.10.robvis-392 · Cited by 213 · see citing works
Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images
Wang, H., Wang, Y., Zhang, Q., Xiang, S., & Pan, C. (2017) · Remote Sensing · doi:10.3390/rs9050446 · Cited by 201 · see citing works
TreeUNet: Adaptive Tree convolutional neural networks for subdecimeter aerial image segmentation
Yue, K., Yang, L., Li, R., Hu, W., Zhang, F., & Li, W. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.07.007 · Cited by 191 · see citing works
Fine-tuning Deep Convolutional Networks for Plant Recognition.
Reyes, A. K., Caicedo, J., & Camargo, J. E. (2015) · CLEF (Working Notes) · Cited by 185 · see citing works
Semantic segmentation of urban scenes by learning local class interactions
Volpi, M., & Ferrari, V. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301377 · Cited by 184 · see citing works
Semantic Segmentation for High Spatial Resolution Remote Sensing Images Based on Convolution Neural Network and Pyramid Pooling Module
Yu, B., Yang, L., & Chen, F. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2860989 · Cited by 184 · see citing works
A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery
Huang, H., Deng, J., Lan, Y., Yang, A., Deng, X., & Zhang, L. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196302 · Cited by 183 · see citing works
Subpixel Urban Land Cover Estimation
Walton, J. T. (2008) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.74.10.1213 · Cited by 181 · see citing works
Multi-Scale Context Aggregation for Semantic Segmentation of Remote Sensing Images
Zhang, J., Lin, S., Ding, L., & Bruzzone, L. (2020) · Remote Sensing · doi:10.3390/rs12040701 · Cited by 172 · see citing works
A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution
Deng, C., & Wu, C. (2013) · Remote Sensing of Environment · doi:10.1016/j.rse.2013.02.005 · Cited by 169 · see citing works
A scale robust convolutional neural network for automatic building extraction from aerial and satellite imagery
Ji, S., Wei, S., & Lü, M. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1528024 · Cited by 157 · see citing works
Incorporating DeepLabv3+ and object-based image analysis for semantic segmentation of very high resolution remote sensing images
Du, S., Du, S., Liu, B., & Zhang, X. (2020) · International Journal of Digital Earth · doi:10.1080/17538947.2020.1831087 · Cited by 157 · see citing works
DenseU-Net-Based Semantic Segmentation of Small Objects in Urban Remote Sensing Images
Dong, R., Pan, X., & Li, F. (2019) · IEEE Access · doi:10.1109/access.2019.2917952 · Cited by 156 · see citing works
Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images
Chen, G., Zhang, X., Wang, Q., Dai, F., Gong, Y., & Zhu, K. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2810320 · Cited by 154 · see citing works
Dynamic Multicontext Segmentation of Remote Sensing Images Based on Convolutional Networks
Nogueira, K., Mura, M. D., Chanussot, J., Schwartz, W. R., & dos Santos, J. A. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2913861 · Cited by 154 · see citing works
Medium Spatial Resolution Satellite Imagery for Estimating and Mapping Urban Impervious Surfaces Using LSMA and ANN
Weng, Q., & Hu, X. (2008) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.917601 · Cited by 149 · see citing works
Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV)
Neupane, B., Horanont, T., & Hung, N. D. (2019) · PLoS ONE · doi:10.1371/journal.pone.0223906 · Cited by 148 · see citing works
Self-citation
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Li, M., & Chen, Z. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.11.006 · Cited by 145 · see citing works
Building segmentation through a gated graph convolutional neural network with deep structured feature embedding
Shi, Y., Li, Q., & Zhu, X. X. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.11.004 · Cited by 143 · see citing works
An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images
Abdollahi, A., Pradhan, B., & Alamri, A. M. (2020) · Geocarto International · doi:10.1080/10106049.2020.1856199 · Cited by 143 · see citing works
Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery
Liu, Y., Nguyen, D. M., Deligiannis, N., Ding, W., & Munteanu, A. (2017) · Remote Sensing · doi:10.3390/rs9060522 · Cited by 141 · see citing works
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Gerke, M. (2015) · University of Twente Research Information · doi:10.13140/2.1.5015.9683 · Cited by 136 · see citing works
Aerial LaneNet: Lane-Marking Semantic Segmentation in Aerial Imagery Using Wavelet-Enhanced Cost-Sensitive Symmetric Fully Convolutional Neural Networks
Azimi, S. M., Fischer, P., Körner, M., & Reinartz, P. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2878510 · Cited by 132 · see citing works
Semantic Labeling of Aerial and Satellite Imagery
Paisitkriangkrai, S., Sherrah, J., Janney, P., & van den Hengel, A. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2582921 · Cited by 131 · see citing works
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Ghorbanzadeh, O., Meena, S. R., Blaschke, T., & Aryal, J. (2019) · Remote Sensing · doi:10.3390/rs11172046 · Cited by 130 · see citing works
Self-citation
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Máttyus, G., Wang, S., Fidler, S., & Urtasun, R. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.197 · Cited by 127 · see citing works
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Lv, Q., Dou, Y., Niu, X., Xu, J., Xu, J., & Xia, F. (2015) · Journal of Sensors · doi:10.1155/2015/538063 · Cited by 126 · see citing works
Land-use classification of remotely sensed data using Kohonen Self-Organizing Feature Map neural networks.
Ji, C.-H. (2000) · Photogrammetric Engineering & Remote Sensing · Cited by 119 · see citing works
Contextually guided very-high-resolution imagery classification with semantic segments
Zhao, W., Du, S., Wang, Q., & Emery, W. J. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.08.011 · Cited by 119 · see citing works
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Shang, R., Zhang, J., Jiao, L., Li, Y., Marturi, N., & Stolkin, R. (2020) · Remote Sensing · doi:10.3390/rs12050872 · Cited by 116 · see citing works
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Liu, W., Su, F., Jin, X., Li, H., & Qin, R. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3035561 · Cited by 115 · see citing works
Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method
Du, Z., Yang, J., Ou, C., & Zhang, T. (2019) · Remote Sensing · doi:10.3390/rs11070888 · Cited by 114 · see citing works
Recurrent Convolutional Neural Networks for Scene ParsingPreprint
Pinheiro, P. H. O., & Collobert, R. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1306.2795 · Cited by 113 · see citing works
Semantic Segmentation on Remotely Sensed Images Using an Enhanced Global Convolutional Network with Channel Attention and Domain Specific Transfer Learning
Panboonyuen, T., Jitkajornwanich, K., Lawawirojwong, S., Srestasathiern, P., & Vateekul, P. (2019) · Remote Sensing · doi:10.3390/rs11010083 · Cited by 112 · see citing works
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Myint, S. W., Giri, C. P., Wang, L., Zhu, Z., & Gillette, S. C. (2008) · GIScience & Remote Sensing · doi:10.2747/1548-1603.45.2.188 · Cited by 106 · see citing works
Improving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours
Griffiths, D., & Boehm, J. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.05.013 · Cited by 105 · see citing works
Dense Semantic Labeling of Very-High-Resolution Aerial Imagery and LiDAR with Fully-Convolutional Neural Networks and Higher-Order CRFs
Liu, Y., Piramanayagam, S., Monteiro, S. T., & Saber, E. S. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.200 · Cited by 100 · see citing works
Object‐based classification of residential land use within Accra, Ghana based on QuickBird satellite data
Stow, D. A., Lopez, A., Lippitt, C. D., Hinton, S., & Weeks, J. R. (2007) · International Journal of Remote Sensing · doi:10.1080/01431160701604703 · Cited by 98 · see citing works
Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images
Volpi, M., & Tuia, D. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.06.007 · Cited by 96 · see citing works
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Chormański, J., de Voorde, T. V., Roeck, T. D., Batelaan, O., & Canters, F. (2008) · Sensors · doi:10.3390/s8020910 · Cited by 94 · see citing works
Supervised Classification of Multisensor Remotely Sensed Images Using a Deep Learning Framework
Piramanayagam, S., Saber, E. S., Schwartzkopf, W. C., & Koehler, F. W. (2018) · Remote Sensing · doi:10.3390/rs10091429 · Cited by 93 · see citing works
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Saito, S., & Aoki, Y. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2083273 · Cited by 89 · see citing works
Semantic Segmentation of Mixed Crops using Deep Convolutional Neural Network
Mortensen, A. K., Dyrmann, M., Karstoft, H., Jørgensen, R. N., & Gislum, R. (2016) · Cited by 88 · see citing works
Fully Convolutional Neural Network with Augmented Atrous Spatial Pyramid Pool and Fully Connected Fusion Path for High Resolution Remote Sensing Image Segmentation
Chen, G., Li, C., Wei, W., Jing, W., Woźniak, M., Blažauskas, T., & Damaševičius, R. (2019) · Applied Sciences · doi:10.3390/app9091816 · Cited by 85 · see citing works
Problems of encoder-decoder frameworks for high-resolution remote sensing image segmentation: Structural stereotype and insufficient learning
Sun, Y. J., Tian, Y. W., & Xu, Y. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.11.051 · Cited by 76 · see citing works
Unsupervised Domain Adaptation Using Generative Adversarial Networks for Semantic Segmentation of Aerial Images
Benjdira, B., Bazi, Y., Koubâa, A., & Ouni, K. (2019) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 70 · see citing works
Recurrent Multiresolution Convolutional Networks for VHR Image Classification
Bergado, J. R., Persello, C., & Stein, A. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2837357 · Cited by 65 · see citing works
Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest
Shadman Roodposhti, M., Aryal, J., Lucieer, A., & Bryan, B. A. (2019) · Entropy · doi:10.3390/e21010078 · Cited by 65 · see citing works
Self-citation
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Flanagan, M., Civco, D. L., & Professor, A. (2001) · Cited by 64 · see citing works
Semantic Labeling of High Resolution Aerial Imagery and LiDAR Data with Fine Segmentation Network
Pan, X., Gao, L., Marinoni, A., Zhang, B., Yang, F., & Gamba, P. E. (2018) · Remote Sensing · doi:10.3390/rs10050743 · Cited by 62 · see citing works
A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images
Li, Y., Xu, L., Rao, J., Guo, L., Yan, Z., & Jin, S. (2019) · Remote Sensing Letters · doi:10.1080/2150704x.2018.1557791 · Cited by 52 · see citing works
Deep cognitive imaging systems enable estimation of continental-scale fire incidence from climate data
Dutta, R., Aryal, J., Das, A., & Kirkpatrick, J. (2013) · Scientific Reports · doi:10.1038/srep03188 · Cited by 48 · see citing works
Self-citation
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Audebert, N., Boulch, A., Randrianarivo, H., Saux, B. L., Ferecatu, M., Lefèvre, S., & Marlet, R. (2017) · Joint Urban Remote Sensing Event (JURSE) · doi:10.1109/jurse.2017.7924536 · Cited by 46 · see citing works
Semantic Segmentation for Urban Planning Maps Based on U-Net
Guo, Z., Shengoku, H., Wu, G., Chen, Q., Yuan, W., Shi, X., Shao, X., Xu, Y., & Shibasaki, R. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8519049 · Cited by 43 · see citing works
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Liu, Y., Piramanayagam, S., Monteiro, S. T., & Saber, E. S. (2019) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.13.016501 · Cited by 40 · see citing works
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Robinson, H., Vimal, S., Khari, M., López, F., & Crespo, R. A. G. (2020) · The International Journal of High Performance Computing Applications · doi:10.1177/1094342020945026 · Cited by 39 · see citing works
Semantic segmentation of high spatial resolution images with deep neural networks
Yang, H., Yu, B., Luo, J., & Chen, F. (2019) · GIScience & Remote Sensing · doi:10.1080/15481603.2018.1564499 · Cited by 38 · see citing works
High-resolution image classification with convolutional networks
Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2017.8128163 · Cited by 38 · see citing works
Feature extraction from satellite images using segnet and fully convolutional networks (FCN)
Sarıtürk, B., Bayram, B., Duran, Z., & Şeker, D. Z. (2020) · International Journal of Engineering and Geosciences · doi:10.26833/ijeg.645426 · Cited by 38 · see citing works
Mask-R-FCN: A Deep Fusion Network for Semantic Segmentation
Zhang, Y., & Chi, M. (2020) · IEEE Access · doi:10.1109/access.2020.3012701 · Cited by 35 · see citing works
Near-Real Time Detection of Beetle Infestation in Pine Forests Using MODIS Data
Anees, A., & Aryal, J. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2330830 · Cited by 34 · see citing works
Self-citation
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Qin, Y., Wu, Y., Li, B., Gao, S., Liu, M., & Zhan, Y. (2019) · Sensors · doi:10.3390/s19051164 · Cited by 33 · see citing works
Learning to semantically segment high-resolution remote sensing images
Nogueira, K., Mura, M. D., Chanussot, J., Schwartz, W. R., & dos Santos, J. A. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900187 · Cited by 30 · see citing works
A Relative Density Ratio-Based Framework for Detection of Land Cover Changes in MODIS NDVI Time Series
Anees, A., Aryal, J., O’Reilly, M. M., & Gale, T. J. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2428306 · Cited by 28 · see citing works
Self-citation
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Anees, A., & Aryal, J. (2014) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2014.2306712 · Cited by 26 · see citing works
Self-citation
SEMANTIC SEGMENTATION OF AERIAL IMAGERY VIA MULTI-SCALE SHUFFLING CONVOLUTIONAL NEURAL NETWORKS WITH DEEP SUPERVISION
Chen, K., Martin Weinmann, A., Sun, X. N., Yan, M., Hinz, S., Jutzi, B., & Martin Weinmann, A. (2018) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-1-29-2018 · Cited by 21 · see citing works
Subpixel Imperviousness Estimation with IKONOS Imagery: An Artificial Neural Network Approach
Mohapatra, R. P., & Wu, C. (2007) · doi:10.1201/9781420043754-7 · Cited by 21 · see citing works
2015 IEEE GRSS Data Fusion Contest: Extremely High Resolution LiDAR and Optical Data [Technical Committees]
Moser, G., Tuia, D., & Shimoni, M. (2015) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2015.2397448 · Cited by 14 · see citing works
Deep Structured Features For Semantic Segmentation
Benini, L., Cavigelli, L., Mentzer, F., Tschannen, M., & Wiatowski, T. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1159385 · Cited by 14 · see citing works
Urban Land Use Classification Based on Aerial and Ground Images
Cao, R., & Qiu, G. (2018) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2018.8516552 · Cited by 14 · see citing works
Classification of multispectral imagery using dynamic learning neural network
Chen, K. S., Tzeng, Y. C., Chen, C.-F., Kao, W. L., & Ni, C. L. (2002) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.1993.322194 · Cited by 11 · see citing works
Object Contra Context: Dual Local-Global Semantic Segmentation in Aerial Images.
Marcu, A., & Leordeanu, M. (2017) · National Conference on Artificial Intelligence · Cited by 8 · see citing works
Semantic segmentation for high-resolution aerial imagery using multi-skip network and Markov random fields
Li, J., Ding, W., Li, H., & Liu, C. (2017) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus.2017.8278309 · Cited by 8 · see citing works
Commitment and typicality measurements for fuzzy ARTMAP neural network
Li, Z., & Eastman, J. R. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.712998 · Cited by 6 · see citing works
SiameseDenseU-Net-based Semantic Segmentation of Urban Remote Sensing Images
Dong, R., Bai, L., & Li, F. (2020) · Mathematical Problems in Engineering · doi:10.1155/2020/1515630 · Cited by 5 · see citing works
An Open-Source UAV Image Processing Web Service for Crop Health Monitoring
Neupane, B., Horanont, T., Duy, H. N., Suebvong, S., & Mahattanawutakorn, T. (2019) · IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) · doi:10.1109/iiai-aai.2019.00014 · Cited by 5 · see citing works
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Wieland, M., Martinis, S., Kiefl, R., & Gstaiger, V. (2023) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113452 · Cited by 194 · see citing works
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A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data
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Zou, X., Yan, Y., Hao, X., Hu, Y., Wen, H., Liu, E., Zhang, J., Li, Y., Li, T., Zheng, Y., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102606 · Cited by 117 · see citing works
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Ahmad, M., Distefano, S., Khan, A., Mazzara, M., Li, C., Li, H., Aryal, J., Ding, Y., Vivone, G., & Hong, D. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130428 · Cited by 107 · see citing works
Self-citation
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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].”
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Advances in Earth observation and machine learning for quantifying blue carbon
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Machine learning versus deep learning in land system science: a decision-making framework for effective land classification
Jane Southworth, B., Smith, A. C., Safaei, M., Rahaman, M., Alruzuq, A., Tefera, B. B., Muir, C. S., & Herrero, H. V. (2024) · Frontiers in Remote Sensing · doi:10.3389/frsen.2024.1374862 · Cited by 43 · see citing works
RETRACTED: A new deep learning approach based on bilateral semantic segmentation models for sustainable estuarine wetland ecosystem management
Pham, H. N., Dang, K. B., Nguyen, T. V., Tran, N. C., Ngo, X. Q., Nguyễn, Đ. A., Phan, T. T. H., Nguyễn, T. T., Guo, W., & Ngo, H. H. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.155826 · Cited by 40 · see citing works
Retracted
Optimised U-Net for Land Use–Land Cover Classification Using Aerial Photography
Clark, A., Phinn, S., & Scarth, P. F. (2023) · PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science · doi:10.1007/s41064-023-00233-3 · Cited by 44 · see citing works
RCCT-ASPPNet: Dual-Encoder Remote Image Segmentation Based on Transformer and ASPP
Li, Y., Cheng, Z., Wang, C., Zhao, J., & Huang, L. (2023) · Remote Sensing · doi:10.3390/rs15020379 · Cited by 36 · see citing works
Remote sensing imagery segmentation in object-based analysis: A review of methods, optimization, and quality evaluation over the past 20 years
Ez-zahouani, B., Teodoro, A. C., Kharki, O. E., Liu, J., Kotaridis, I., Yuan, X., & Ma, L. (2023) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2023.101031 · Cited by 35 · see citing works
A Review on Recent Deep Learning-Based Semantic Segmentation for Urban Greenness Measurement
Lee, D. H., Park, H. Y., & Lee, J. (2024) · Sensors · doi:10.3390/s24072245 · Cited by 33 · see citing works
A Combined Convolutional Neural Network for Urban Land-Use Classification with GIS Data
Yu, J., Zeng, P., Yu, Y., Yu, H., Huang, L., & Zhou, D. (2022) · Remote Sensing · doi:10.3390/rs14051128 · Cited by 34 · see citing works
Mapping Roofing with Asbestos-Containing Material by Using Remote Sensing Imagery and Machine Learning-Based Image Classification: A State-of-the-Art Review
Abbasi, M., Mostafa, S. F., Vieira, A. S., Patorniti, N. P., & Stewart, R. A. (2022) · Sustainability · doi:10.3390/su14138068 · Cited by 30 · see citing works
MAFF-HRNet: Multi-Attention Feature Fusion HRNet for Building Segmentation in Remote Sensing Images
Che, Z., Shen, L., Huo, L., Hu, C., Wang, Y., Lu, Y., & Bi, F. (2023) · Remote Sensing · doi:10.3390/rs15051382 · Cited by 30 · see citing works
Real-Time Semantic Segmentation: A brief survey and comparative study in remote sensing
Broni-Bediako, C., Xia, J., & Yokoya, N. (2023) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2023.3321258 · Cited by 30 · see citing works
Evaluating green cover and open spaces in informal settlements of Mumbai using deep learning
Dabra, A., & Kumar, V. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08320-7 · Cited by 26 · see citing works
Multiscale Semantic Segmentation of Remote Sensing Images Based on Edge Optimization
Huang, W., Deng, F., Liu, H., Ding, M., & Yao, Q. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3553524 · Cited by 27 · see citing works
Monitoring the green evolution of vernacular buildings based on deep learning and multi-temporal remote sensing images
Wen, B., Peng, F., Yang, Q., Lu, T., Bai, B., Wu, S., & Xu, F. (2022) · Building Simulation · doi:10.1007/s12273-022-0927-7 · Cited by 25 · see citing works
DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence eraPreprint
Restrepo, D., Wu, C.-W., Vásquez-Venegas, C., Nakayama, L., Celi, L., & Lopez, D. M. (2024) · Research Square · doi:10.21203/rs.3.rs-4277992/v1 · Cited by 25 · see citing works
SegMind: Semisupervised Remote Sensing Image Semantic Segmentation With Masked Image Modeling and Contrastive Learning Method
Li, Z., Chen, H., Wu, J., Li, J., & Jing, N. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3321041 · Cited by 24 · see citing works
MDPrePost-Net: A Spatial-Spectral-Temporal Fully Convolutional Network for Mapping of Mangrove Degradation Affected by Hurricane Irma 2017 Using Sentinel-2 Data
Jamaluddin, I., Thaipisutikul, T., Chen, Y.-N., Chuang, C.-H., & Hu, C. (2021) · Remote Sensing · doi:10.3390/rs13245042 · Cited by 24 · see citing works
Dynamic Mapping of Inland Freshwater Aquaculture Areas in Jianghan Plain, China
Han, Y., Huang, J., Ling, F., Qiu, J., Liu, Z., Li, X., Chang, C., & Chi, H. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3269430 · Cited by 24 · see citing works
Land Use/Land Cover Remote Sensing Classification in Complex Subtropical Karst Environments: Challenges, Methodological Review, and Research Frontiers
Huang, D., Zhou, Z., Zhang, Z., Dai, Q., Lu, H., Li, Y., & Huang, Y. (2025) · Applied Sciences · doi:10.3390/app15179641 · Cited by 23 · see citing works
A Comprehensive Review of the Recent Advancement in Integrating Deep Learning with Geographic Information Systems
Raihan, A. (2023) · Research Briefs on Information and Communication Technology Evolution · doi:10.56801/rebicte.v9i.160 · Cited by 22 · see citing works
A multi-stage adaptive otsu thresholding algorithm for pore segmentation in rock thin-section images
Yu, C., Wu, W., Zheng, J., Zeng, W., Zheng, D., Li, Z., Chen, C., & Wang, S. (2025) · Earth Science Informatics · doi:10.1007/s12145-025-01716-0 · Cited by 22 · see citing works
HFENet: Hierarchical Feature Extraction Network for Accurate Landcover Classification
Wang, D., Yang, R., Liu, H., He, H., Tan, J., Li, S., Qiao, Y., Tang, K., & Wang, X. (2022) · Remote Sensing · doi:10.3390/rs14174244 · Cited by 22 · see citing works
Deep-Learning-Based Semantic Segmentation for Remote Sensing: A Bibliometric Literature Review
Hasan, K. R., Tuli, A., Khan, M., Kee, S.-H., Samad, M. A., & Nahid, A. A. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3328315 · Cited by 21 · see citing works
Building Extraction From High Spatial Resolution Remote Sensing Images of Complex Scenes by Combining Region-Line Feature Fusion and OCNN
Dong, D., Ming, D., Weng, Q., Yang, Y., Fang, K., Xu, L. T., Du, T., Zhang, Y., & Liu, R. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3273726 · Cited by 22 · see citing works
Generative adversarial networks assisted machine learning based automated quantification of grain size from scanning electron microscope back scatter images
Anantatamukala, A., Krishna, K. V. M., & Dahotre, N. B. (2023) · Materials Characterization · doi:10.1016/j.matchar.2023.113396 · Cited by 21 · see citing works
Evaluation of urban underground space via automated constraint identification and hybrid analysis
Deng, F. F., Cheng, T., Huang, Y., Chen, Z., & Han, Q. (2024) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2024.106005 · Cited by 20 · see citing works
Intelligent Traffic Monitoring through Heterogeneous and Autonomous Networks Dedicated to Traffic Automation
Zadobrischi, E. (2022) · Sensors · doi:10.3390/s22207861 · Cited by 20 · see citing works
Hybrid Shunted Transformer embedding UNet for remote sensing image semantic segmentation
Zhou, H., Xiao, X., Li, H., Liu, X., & Liang, P. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09888-4 · Cited by 20 · see citing works
Machine learning-ready remote sensing data for Maya archaeology
Kokalj, Ž., Džeroski, S., Šprajc, I., Štajdohar, J., Draksler, A., & Somrak, M. (2023) · Scientific Data · doi:10.1038/s41597-023-02455-x · Cited by 20 · see citing works
CNNs for remote extraction of urban features: A survey-driven benchmarking
Neupane, B., Aryal, J., & Rajabifard, A. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124751 · Cited by 20 · see citing works
Self-citation
Benchmark for Building Segmentation on Up-Scaled Sentinel-2 Imagery
Illarionova, S., Shadrin, D., Shukhratov, I., Evteeva, K., Popandopulo, G., Sotiriadi, N., Oseledets, I. V., & Burnaev, E. V. (2023) · Remote Sensing · doi:10.3390/rs15092347 · Cited by 19 · see citing works
Land Cover Classification Model Using Multispectral Satellite Images Based on a Deep Learning Synergistic Semantic Segmentation Network
Gharahbagh, A. A., Hajihashemi, V., Machado, J. J. M., & Tavares, J. M. R. S. (2025) · Sensors · doi:10.3390/s25071988 · Cited by 17 · see citing works
SEMANTIC SEGMENTATION OF UAV IMAGES BASED ON U-NET IN URBAN AREA
Majidizadeh, A., Hasani, H., & Jafari, M. (2023) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-x-4-w1-2022-451-2023 · Cited by 17 · see citing works
Machine learning based de-noising of electron back scatter patterns of various crystallographic metallic materials fabricated using laser directed energy deposition
Krishna, K. V. M., Madhavan, R., Pantawane, M. V., Banerjee, R., & Dahotre, N. B. (2023) · Ultramicroscopy · doi:10.1016/j.ultramic.2023.113703 · Cited by 17 · see citing works
A K-Net-based hybrid semantic segmentation method for extracting lake water bodies
Chen, C., Wang, Y., Yang, S., Ji, X., & Wang, G. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106904 · Cited by 17 · see citing works
Semantic Segmentation of Remote Sensing Images: Definition, Methods, Datasets and Applications
Spasev, V., Dimitrovski, I., Kitanovski, I., & Chorbev, I. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-54321-0_9 · Cited by 17 · see citing works
Review of Artificial Intelligence Applications for Virtual Sensing of Underground Utilities
Oguntoye, K. S., Laflamme, S., Sturgill, R. E., & Eisenmann, D. J. (2023) · Sensors · doi:10.3390/s23094367 · Cited by 16 · see citing works
Estimation of NPP in Huangshan District Based on Deep Learning and CASA Model
Wang, Z., Zhou, Y., Sun, X., & Xu, Y. (2024) · Forests · doi:10.3390/f15081467 · Cited by 16 · see citing works
Multi-Scale Feature Map Aggregation and Supervised Domain Adaptation of Fully Convolutional Networks for Urban Building Footprint Extraction
Aryal, J., & Neupane, B. (2023) · Remote Sensing · doi:10.3390/rs15020488 · Cited by 15 · see citing works
Self-citation
Toward Integrated Urban Observatories: Synthesizing Remote and Social Sensing in Urban Science
Yu, D. (2025) · Remote Sensing · doi:10.3390/rs17122041 · Cited by 15 · see citing works
A Novel Boundary Loss Function in Deep Convolutional Networks to Improve the Buildings Extraction From High-Resolution Remote Sensing Images
Hosseinpour, H., Samadzadegan, F., & Javan, F. D. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3178470 · Cited by 15 · see citing works
Improving Semantic Segmentation of Roof Segments Using Large-Scale Datasets Derived from 3D City Models and High-Resolution Aerial Imagery
Faltermeier, F. L., Krapf, S., Willenborg, B., & Kolbe, T. H. (2023) · Remote Sensing · doi:10.3390/rs15071931 · Cited by 15 · see citing works
AiTLAS: Artificial Intelligence Toolbox for Earth Observation
Dimitrovski, I., Kitanovski, I., Panov, P., Kostovska, A., Simidjievski, N., & Kocev, D. (2023) · Remote Sensing · doi:10.3390/rs15092343 · Cited by 16 · see citing works
MPFFNet: LULC classification model for high-resolution remote sensing images with multi-path feature fusion
Yuan, H., Zhang, Z., Rong, X., Feng, D., Zhang, S., & Yang, S. (2023) · International Journal of Remote Sensing · doi:10.1080/01431161.2023.2261153 · Cited by 15 · see citing works
Enhanced Dual-Channel Model-Based with Improved Unet++ Network for Landslide Monitoring and Region Extraction in Remote Sensing Images
Junxin, W., Zhang, Q. T., Xie, H., Chen, Y., & Sun, R. (2024) · Remote Sensing · doi:10.3390/rs16162990 · Cited by 15 · see citing works
Assessment of Rooftop Photovoltaic Potential Considering Building Functions
Zhang, Z., Pu, Y., Sun, Z., Qian, Z., & Chen, M. (2024) · Remote Sensing · doi:10.3390/rs16162993 · Cited by 14 · see citing works
DBRSNet: a dual-branch remote sensing image segmentation model based on feature interaction and multi-scale feature fusion
Ji, Y., Shi, W., Lei, J., & Ding, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-13236-4 · Cited by 14 · see citing works
DeepTriNet: A Tri-Level Attention-Based DeepLabv3+ Architecture for Semantic Segmentation of Satellite Images
Ovi, T. B., Mosharrof, S., Bashree, N., Islam, M. N., & Islam, M. S. (2024) · Smart innovation, systems and technologies · doi:10.1007/978-981-99-7711-6_30 · Cited by 13 · see citing works
Harnessing Deep Learning and Satellite Imagery for Post-Buyout Land Cover Mapping
Otal, H. T., Zavar, E. M., Binder, S. B., Greer, A., & Canbaz, M. A. (2024) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai59869.2024.00130 · Cited by 13 · see citing works
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Cira, C.-I., Callejo, M. Á. M., Alcarria, R., Iturrioz, T., & Justel, J. J. A. (2024) · Remote Sensing · doi:10.3390/rs16162954 · Cited by 13 · see citing works
Deep Learning Approaches for Wildfire Severity Prediction: A Comparative Study of Image Segmentation Networks and Visual Transformers on the EO4WildFires Dataset
Sykas, D., Zografakis, D., & Demestichas, K. P. (2024) · Fire · doi:10.3390/fire7110374 · Cited by 13 · see citing works
Parcel-Level Flood and Drought Detection for Insurance Using Sentinel-2A, Sentinel-1 SAR GRD and Mobile Images
Thapa, A., Horanont, T., & Neupane, B. (2022) · Remote Sensing · doi:10.3390/rs14236095 · Cited by 12 · see citing works
Self-citation
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Karila, K., Leena Matikainen, A., Karjalainen, M., Puttonen, E., Chen, Y., & Hyyppa, J. M. (2023) · ISPRS Open Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.ophoto.2023.100046 · Cited by 12 · see citing works
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Wu, T., Wang, C., Li, J., & Huang, C. (2023) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc56721.2023.10294072 · Cited by 12 · see citing works
DESformer: A Dual-Branch Encoding Strategy for Semantic Segmentation of Very-High-Resolution Remote Sensing Images Based on Feature Interaction and Multiscale Context Fusion
Liu, W., Cui, N., Guo, L., Du, S., & Wang, W. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3446628 · Cited by 12 · see citing works
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Kanagavelu, R., Dua, K., Garai, P., Thomas, N., Elias, S., Elias, S., Wei, Q., Yong, L., & Rick, G. S. M. (2023) · Electronics · doi:10.3390/electronics12040896 · Cited by 11 · see citing works
Dynamic High-Resolution Network for Semantic Segmentation in Remote-Sensing Images
Guo, S., Yang, Q., Xiang, S., Wang, P., & Wang, X. (2023) · Remote Sensing · doi:10.3390/rs15092293 · Cited by 11 · see citing works
Lightweight convolutional neural networks with context broadcast transformer for real-time semantic segmentation
Hu, K., Xie, Z., & Hu, Q. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105053 · Cited by 11 · see citing works
Semi-supervised recognition for artificial intelligence assisted pathology image diagnosis
Pan, Y., Gou, F., Xiao, C., Liu, J., & Zhou, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-70750-7 · Cited by 11 · see citing works
HexaLCSeg: A historical benchmark dataset from Hexagon satellite images for land cover segmentation [Software and Data Sets]
Sertel, E., Kabadayı, M. E., Şengül, G. S., & Tumer, I. N. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3394248 · Cited by 11 · see citing works
Wall segmentation in 2D images using convolutional neural networks
Bjekić, M., Lazović, A., Venkatachalam, K., Bačanin, N., Zivkovic, M. Z., Kvaščev, G., & Nikolić, B. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1565 · Cited by 11 · see citing works
Joint superpixel and Transformer for high resolution remote sensing image classification
Dang, G., Mao, Z., Zhang, T., Liu, T., Wang, T., Li, L., Gao, Y., Tian, R., Wang, K., & Han, L. (2024) · Scientific Reports · doi:10.1038/s41598-024-55482-y · Cited by 11 · see citing works
Remote Sensing Image Semantic Segmentation Algorithm Based on Improved ENet Network
Wang, Y. (2021) · Scientific Programming · doi:10.1155/2021/5078731 · Cited by 11 · see citing works
Memory-Augmented Transformer for Remote Sensing Image Semantic Segmentation
Zhao, X., Guo, J., Zhang, Y., & Wu, Y. (2021) · Remote Sensing · doi:10.3390/rs13224518 · Cited by 11 · see citing works
A deep transfer learning-based damage assessment on post-event very high-resolution orthophotos
Abdi, G., Esfandiari, M., & Jabari, S. (2021) · GEOMATICA · doi:10.1139/geomat-2021-0014 · Cited by 11 · see citing works
Recent advances in scene image representation and classification
Sitaula, C., Shahi, T. B., Marzbanrad, F., & Aryal, J. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15005-9 · Cited by 10 · see citing works
Self-citation
Combining Local Knowledge with Object-Based Machine Learning Techniques for Extracting Informal Settlements from Very High-Resolution Satellite Data
Alrasheedi, K. G., Dewan, A. M., & El‐Mowafy, A. (2024) · Earth Systems and Environment · doi:10.1007/s41748-024-00393-1 · Cited by 10 · see citing works
Remote sensing for environmentally responsive urban built environment: A review of tools, methods and gaps
Manapragada, N. V. S. K., Mandelmilch, M. E., Roitberg, E., Kizel, F., & Natanian, J. (2025) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2025.101529 · Cited by 10 · see citing works
Espresso: A Global Deep Learning Model to Estimate Precipitation from Satellite Observations
Berthomier, L., & Perier, L. (2023) · Meteorology · doi:10.3390/meteorology2040025 · Cited by 10 · see citing works
Deep Learning-Based Semantic Segmentation of Urban Areas Using Heterogeneous Unmanned Aerial Vehicle Datasets
Song, A. (2023) · Aerospace · doi:10.3390/aerospace10100880 · Cited by 9 · see citing works
Dual-resolution transformer combined with multi-layer separable convolution fusion network for real-time semantic segmentation
Hu, K., Xie, Z., & Hu, Q. (2023) · Computers & Graphics · doi:10.1016/j.cag.2023.12.015 · Cited by 9 · see citing works
Classification of ground information during wildfires: Using an improved UNet model
Wei, S., Hao, H., Luo, M., & Yang, Z. (2024) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2024.105157 · Cited by 8 · see citing works

Intelligent Image Semantic Segmentation: A Review Through Deep Learning Techniques for Remote Sensing Image Analysis
Jiang, B., An, X., Xu, S., & Chen, Z. (2022) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-022-01496-w · Cited by 50 · see citing works
Deep-Learning-Based Semantic Segmentation of Remote Sensing Images: A Survey
Huang, L., Jiang, B., Lv, S., Liu, Y., & Fu, Y. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3335891 · Cited by 175 · see citing works
A review of deep learning methods for semantic segmentation of remote sensing imagery
Yuan, X., Shi, J., & Gu, L. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114417 · Cited by 780 · see citing works
Deep learning-based semantic segmentation of remote sensing images: a review
Lv, J., Shen, Q., Lv, M., Li, Y., Shi, L., & Zhang, P. (2023) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2023.1201125 · Cited by 104 · see citing works
Remote Sensing Image Classification and Semantic Segmentation
Li, J., Du, Q., Chanussot, J., Li, W., Xi, B., Song, R., & Li, Y. (2024) · MDPI eBooks · doi:10.3390/books978-3-7258-1366-7 · Cited by 2 · see citing works

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 · 2012: 1
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 › Engineering › Media Technology › Remote-Sensing Image Classification
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Engineering › Ocean Engineering › 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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