SEMANTIC SEGMENTATION OF AERIAL IMAGES WITH AN ENSEMBLE OF CNNS

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1. An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images A. Abdollahi et al. https://doi.org/10.1080/10106049.2020.1856199
2. On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification A. Sebastianelli et al. https://doi.org/10.1109/JSTARS.2021.3134785
3. Extraction and spatiotemporal analysis of impervious surfaces in Chongqing based on enhanced DeepLabv3+ D. Wei et al. https://doi.org/10.1038/s41598-025-94882-6
4. Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot) N. Narisetti et al. https://doi.org/10.3389/fpls.2022.906410
5. Uncertainty-Based Human-in-the-Loop Deep Learning for Land Cover Segmentation C. García Rodríguez et al. https://doi.org/10.3390/rs12223836
6. Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery S. Bachhofner et al. https://doi.org/10.3390/rs12081289
7. Aerial and Optical Images-Based Plant Species Segmentation Using Enhancing Nested Downsampling Features C. Lin et al. https://doi.org/10.3390/f12121695
8. HENet: Head-Level Ensemble Network for Very High Resolution Remote Sensing Images Semantic Segmentation Y. Cao et al. https://doi.org/10.1109/LGRS.2022.3147857
9. Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area G. Tariku et al. https://doi.org/10.3390/app15020871
10. CscGAN: Conditional Scale-Consistent Generation Network for Multi-Level Remote Sensing Image to Map Translation Y. Liu et al. https://doi.org/10.3390/rs13101936
11. Planetary Mapping Using Deep Learning: A Method to Evaluate Feature Identification Confidence Applied to Habitats in Mars-Analog Terrain M. Phillips et al. https://doi.org/10.1089/ast.2022.0014
12. Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images T. Pham et al. https://doi.org/10.1136/bjophthalmol-2019-315723
13. Multiclass Land Cover Mapping from Historical Orthophotos Using Domain Adaptation and Spatio-Temporal Transfer Learning W. Van den Broeck et al. https://doi.org/10.3390/rs14235911
14. Identifying Informal Settlements Using Contourlet Assisted Deep Learning R. Ansari et al. https://doi.org/10.3390/s20092733
15. U-Net Ensemble for Enhanced Semantic Segmentation in Remote Sensing Imagery I. Dimitrovski et al. https://doi.org/10.3390/rs16122077
16. Machine learning in the documentation and analysis of historical landmark buildings: a systematic review and practical demonstration Z. Huang https://doi.org/10.1186/s43238-026-00254-y
17. Portrait Segmentation Using Ensemble of Heterogeneous Deep-Learning Models Y. Kim et al. https://doi.org/10.3390/e23020197
18. PGNet: Positioning Guidance Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Images B. Liu et al. https://doi.org/10.3390/rs14174219
19. Convolutional Neural Network for Thailand's Eastern Economic Corridor (EEC) land cover classification using overlapping process on satellite images P. Chermprayong et al. https://doi.org/10.1016/j.rsase.2021.100543
20. DensePPMUNet-a: A Robust Deep Learning Network for Segmenting Water Bodies From Aerial Images D. Xiang et al. https://doi.org/10.1109/TGRS.2023.3251659
21. FVPNet: A Fuzzy Visual Positioning Perception Network for Small Object Segmentation in Remote Sensing Images Y. Yan et al. https://doi.org/10.1109/JSTARS.2025.3585184
22. C3Net: Cross-Modal Feature Recalibrated, Cross-Scale Semantic Aggregated and Compact Network for Semantic Segmentation of Multi-Modal High-Resolution Aerial Images Z. Cao et al. https://doi.org/10.3390/rs13030528
23. An End-to-End Atrous Spatial Pyramid Pooling and Skip-Connections Generative Adversarial Segmentation Network for Building Extraction from High-Resolution Aerial Images M. Yu et al. https://doi.org/10.3390/app12105151
24. Urban Land-Cover Classification Using Side-View Information from Oblique Images C. Xiao et al. https://doi.org/10.3390/rs12030390
25. Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters Y. Xu et al. https://doi.org/10.3390/rs10010144
26. Building Extraction of Aerial Images by a Global and Multi-Scale Encoder-Decoder Network J. Ma et al. https://doi.org/10.3390/rs12152350
27. Fully‐connected semantic segmentation of hyperspectral and LiDAR data H. Aytaylan & S. Yuksel https://doi.org/10.1049/iet-cvi.2018.5067
28. A review of deep learning methods for semantic segmentation of remote sensing imagery X. Yuan et al. https://doi.org/10.1016/j.eswa.2020.114417
29. A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication A. Larsen et al. https://doi.org/10.1038/s41370-020-0246-y
30. Deep learning based high performance classification architecture for low-altitude aerial images P. Mittal et al. https://doi.org/10.1007/s11042-023-16195-y
31. Convolutional Neural Network for Convolution of Aerial Survey Images N. Trong et al. https://doi.org/10.1016/j.ifacol.2021.10.513
32. Extraction of urban multi-class from high-resolution images using pyramid generative adversarial networks R. Alshehhi & P. Marpu https://doi.org/10.1016/j.jag.2021.102379
33. High resolution remote sensing recognition of elm sparse forest via deep-learning-based semantic segmentation H. Liu et al. https://doi.org/10.1016/j.ecolind.2024.112428
34. Domain Adaptation for Semantic Segmentation of Historical Panchromatic Orthomosaics in Central Africa N. Mboga et al. https://doi.org/10.3390/ijgi10080523
35. Geoscience-aware deep learning: A new paradigm for remote sensing Y. Ge et al. https://doi.org/10.1016/j.srs.2022.100047
36. Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery S. Crommelinck et al. https://doi.org/10.3390/rs11212505
37. From multi-sensor aerial data to thermal and infrared simulation of semantic 3D models: Towards identification of urban heat islands D. Bulatov et al. https://doi.org/10.1016/j.infrared.2020.103233
38. TerraDA: A Domain-Adaptive Framework With Dynamic Multimodal Remote Sensing Data Fusion and Consistency Learning for Cross-City Land Cover Classification W. Wu et al. https://doi.org/10.1109/JSTARS.2025.3608777
39. Dense Semantic Labeling with Atrous Spatial Pyramid Pooling and Decoder for High-Resolution Remote Sensing Imagery Y. Wang et al. https://doi.org/10.3390/rs11010020
40. ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data F. Diakogiannis et al. https://doi.org/10.1016/j.isprsjprs.2020.01.013
41. Hierarchical spatial features learning with deep CNNs for very high-resolution remote sensing image classification G. Zhang et al. https://doi.org/10.1080/01431161.2018.1506593
42. Exploiting semantic segmentation to boost reinforcement learning in video game environments J. Montalvo et al. https://doi.org/10.1007/s11042-022-13695-1
43. Semantic Segmentation of Some Rock-Forming Mineral Thin Sections Using Deep Learning Algorithms: A Case Study from the Nikeiba Area, South Eastern Desert, Egypt S. Hassan et al. https://doi.org/10.3390/rs16132276
44. Combining Deep Semantic Segmentation Network and Graph Convolutional Neural Network for Semantic Segmentation of Remote Sensing Imagery S. Ouyang & Y. Li https://doi.org/10.3390/rs13010119
45. A Deep Learning-Based Framework for Automated Extraction of Building Footprint Polygons from Very High-Resolution Aerial Imagery Z. Li et al. https://doi.org/10.3390/rs13183630
46. Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review D. Popescu et al. https://doi.org/10.3389/fpls.2023.1237695
47. Bayesian U-Net: Estimating Uncertainty in Semantic Segmentation of Earth Observation Images C. Dechesne et al. https://doi.org/10.3390/rs13193836
48. Instance-Level Scaling and Dynamic Margin-Alignment Knowledge Distillation for Remote Sensing Image Scene Classification C. Li et al. https://doi.org/10.3390/rs16203853
49. Automatic and Semantically-Aware 3D UAV Flight Planning for Image-Based 3D Reconstruction T. Koch et al. https://doi.org/10.3390/rs11131550
50. FURSformer: Semantic Segmentation Network for Remote Sensing Images with Fused Heterogeneous Features Z. Zhang et al. https://doi.org/10.3390/electronics12143113
51. A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and Transferability R. Qin & T. Liu https://doi.org/10.3390/rs14030646
52. No cell is an island: characterising the leaf epidermis usingepidermalmorph,a new R package M. Brown & G. Jordan https://doi.org/10.1111/nph.18519
53. Fine-Grained Abandoned Cropland Mapping in Southern China Using Pixel Attention Contrastive Learning H. Li et al. https://doi.org/10.1109/JSTARS.2023.3338454
54. Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications F. Mena et al. https://doi.org/10.1109/JSTARS.2024.3361556
55. Assessing Deep Convolutional Neural Networks and Assisted Machine Perception for Urban Mapping Y. Shao et al. https://doi.org/10.3390/rs13081523
56. CCANet: Class-Constraint Coarse-to-Fine Attentional Deep Network for Subdecimeter Aerial Image Semantic Segmentation G. Deng et al. https://doi.org/10.1109/TGRS.2021.3055950
57. Effective Fusion of Multi-Modal Remote Sensing Data in a Fully Convolutional Network for Semantic Labeling W. Zhang et al. https://doi.org/10.3390/rs10010052
58. Multi‐task fully convolutional networks for building segmentation on SAR image Z. Zhang et al. https://doi.org/10.1049/joe.2019.0569
59. Fully automatic image segmentation based on FCN and graph cuts Z. Wang et al. https://doi.org/10.1007/s00530-022-00945-3
60. Classification of satellite images for ecology management using deep features obtained from convolutional neural network models E. Özbay & M. Yıldırım https://doi.org/10.1007/s42044-022-00133-6
61. Training Small Networks for Scene Classification of Remote Sensing Images via Knowledge Distillation G. Chen et al. https://doi.org/10.3390/rs10050719
62. Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks R. Guo et al. https://doi.org/10.3390/ijgi7030110
63. Fusion of hierarchical class graphs for remote sensing semantic segmentation X. Kang et al. https://doi.org/10.1016/j.inffus.2024.102409
64. A Novel Object-Based Deep Learning Framework for Semantic Segmentation of Very High-Resolution Remote Sensing Data: Comparison with Convolutional and Fully Convolutional Networks M. Papadomanolaki et al. https://doi.org/10.3390/rs11060684
65. A Modification method based on U-Net for the distorted pseudo edge of aerial initial orthophoto J. Xiong et al. https://doi.org/10.1088/1742-6596/1922/1/012006
66. Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data S. Liu et al. https://doi.org/10.3390/rs11060690
67. FMnet: Iris Segmentation and Recognition by Using Fully and Multi-Scale CNN for Biometric Security R. Tobji et al. https://doi.org/10.3390/app9102042
68. A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images X. Wang et al. https://doi.org/10.1016/j.media.2020.101914
69. Building Multi-Feature Fusion Refined Network for Building Extraction from High-Resolution Remote Sensing Images S. Ran et al. https://doi.org/10.3390/rs13142794
70. Comparing the Semantic Segmentation of High-Resolution Images Using Deep Convolutional Networks: SegNet, HRNet, CSE-HRNet and RCA-FCN N. Sadeghi et al. https://doi.org/10.61186/jist.39680.11.44.359
71. FactSeg: Foreground Activation-Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery A. Ma et al. https://doi.org/10.1109/TGRS.2021.3097148
72. Semantic Segmentation of Satellite Images using 2 various U-Net Architectures: A Comparison Study N. Gonthina & . L.V. Narasimha Prasad https://doi.org/10.22399/ijcesen.1360
73. Impact of Thailand’s Eastern Economic Corridor (EEC) Policy on Land Use: Prediction Up to 2040 by Combining CNN Land Cover and Machine Learning Methods on Socio-Economic Data D. Gomez et al. https://doi.org/10.1007/s12061-025-09672-4
74. Building Corner Detection in Aerial Images with Fully Convolutional Networks W. Song et al. https://doi.org/10.3390/s19081915
75. Automatic Building Footprint Extraction from Multi-Resolution Remote Sensing Images Using a Hybrid FCN P. Schuegraf & K. Bittner https://doi.org/10.3390/ijgi8040191
76. Fully-automated root image analysis (faRIA) N. Narisetti et al. https://doi.org/10.1038/s41598-021-95480-y
77. Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Review S. Muchuchuti & S. Viriri https://doi.org/10.3390/jimaging9040084
78. Semantic Segmentation of Remote-Sensing Images Through Fully Convolutional Neural Networks and Hierarchical Probabilistic Graphical Models M. Pastorino et al. https://doi.org/10.1109/TGRS.2022.3141996
79. Nonlocal CNN SAR Image Despeckling D. Cozzolino et al. https://doi.org/10.3390/rs12061006
80. Improved U-Net With Identity Transformer Encoder for Efficient UAV Semantic Segmentation T. do et al. https://doi.org/10.1109/ACCESS.2025.3638695
81. Multi-Modality and Multi-Scale Attention Fusion Network for Land Cover Classification from VHR Remote Sensing Images T. Lei et al. https://doi.org/10.3390/rs13183771
82. JSH-Net: joint semantic segmentation and height estimation using deep convolutional networks from single high-resolution remote sensing imagery B. Zhang et al. https://doi.org/10.1080/01431161.2022.2135410
83. High-Movement Human Segmentation in Video Using Adaptive N-Frames Ensemble Y. Kim et al. https://doi.org/10.32604/cmc.2022.028632
84. LoveNAS: Towards multi-scene land-cover mapping via hierarchical searching adaptive network J. Wang et al. https://doi.org/10.1016/j.isprsjprs.2024.01.011
85. Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data A. Gebrehiwot et al. https://doi.org/10.3390/s19071486
86. Performance evaluation of three deep learning models in building footprint extraction from aerial and satellite images N. Ahmadian et al. https://doi.org/10.61186/jgit.11.1.105
87. Real-Time Dense Semantic Labeling with Dual-Path Framework for High-Resolution Remote Sensing Image Y. Wang et al. https://doi.org/10.3390/rs11243020
88. Toward the automatic detection of effective gerbil holes in desert grasslands through unmanned aerial vehicle imagery G. Qi et al. https://doi.org/10.1080/10106049.2023.2215722
89. A multi-class deep learning segmentation approach for automated analysis of axial and lateral roots in barley plants N. Narisetti et al. https://doi.org/10.1016/j.compag.2025.111157
90. Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis B. Neupane et al. https://doi.org/10.3390/rs13040808
91. Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images M. Zhang et al. https://doi.org/10.3390/rs9050500
92. DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning S. Yang et al. https://doi.org/10.1016/j.gpb.2022.12.007
93. Transformer and CNN Hybrid Deep Neural Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery C. Zhang et al. https://doi.org/10.1109/TGRS.2022.3144894
94. Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images N. Audebert et al. https://doi.org/10.3390/rs9040368
95. A real-time efficient object segmentation system based on U-Net using aerial drone images I. Ahmed et al. https://doi.org/10.1007/s11554-021-01166-z
96. High-Resolution U-Net: Preserving Image Details for Cultivated Land Extraction W. Xu et al. https://doi.org/10.3390/s20154064