THE ISPRS BENCHMARK ON URBAN OBJECT CLASSIFICATION AND 3D BUILDING RECONSTRUCTION

297 citations as recorded by crossref.

1. Multiscale and Multifeature Segmentation of High-Spatial Resolution Remote Sensing Images Using Superpixels with Mutual Optimal Strategy Z. Fu et al. https://doi.org/10.3390/rs10081289
2. Automatic representation and reconstruction of DBM from LiDAR data using Recursive Minimum Bounding Rectangle E. Kwak & A. Habib https://doi.org/10.1016/j.isprsjprs.2013.10.003
3. 基于全局-局部Mamba与文本引导的多模态遥感图像语义分割模型 陈. Chen Siyuan & 汪. Wang Xili https://doi.org/10.3788/LOP251040
4. Matching Aerial Images to 3D Building Models Using Context-Based Geometric Hashing J. Jung et al. https://doi.org/10.3390/s16060932
5. Semantic Segmentation and Building Extraction from Airborne LiDAR Data with Multiple Return Using PointNet++ Y. Shin et al. https://doi.org/10.3390/app12041975
6. Mapping Rice Paddies in Complex Landscapes with Convolutional Neural Networks and Phenological Metrics S. Zhao et al. https://doi.org/10.1080/15481603.2019.1658960
7. Deep learning based computer vision under the prism of 3D point clouds: a systematic review K. Tychola et al. https://doi.org/10.1007/s00371-023-03237-7
8. Height estimation from single aerial imagery using contrastive learning based multi-scale refinement network W. Zhao et al. https://doi.org/10.1080/17538947.2023.2225881
9. LANet: Lightweight Attention-Mamba Network for Semantic Segmentation of High-Resolution Remote Sensing Images Q. Meng et al. https://doi.org/10.1109/LGRS.2026.3676266
10. RSUS: A Novel Upsampling Layer for Semantic Segmentation Network of Remote Sensing Images Y. Liu et al. https://doi.org/10.1109/TGRS.2026.3714102
11. DSSM: A Deep Neural Network with Spectrum Separable Module for Multi-Spectral Remote Sensing Image Segmentation H. Zhu et al. https://doi.org/10.3390/rs14040818
12. RID—Roof Information Dataset for Computer Vision-Based Photovoltaic Potential Assessment S. Krapf et al. https://doi.org/10.3390/rs14102299
13. Semantic Segmentation of Multispectral Images via Linear Compression of Bands: An Experiment Using RIT-18 Y. Cai et al. https://doi.org/10.3390/rs14112673
14. LIE-DSM: Leveraging Single Remote Sensing Imagery to Enhance Digital Surface Model Resolution S. Zhang et al. https://doi.org/10.1109/TGRS.2024.3408793
15. An Uncertainty Descriptor for Quantitative Measurement of the Uncertainty of Remote Sensing Images Q. Zhang & P. Zhang https://doi.org/10.3390/rs11131560
16. Boundary Extraction Based on Dual Stream Deep Learning Model in High Resolution Remote Sensing Images Ö. Akçay et al. https://doi.org/10.28979/jarnas.911130
17. Vehicle Detection and Attribution from a Multi-Sensor Dataset Using a Rule-Based Approach Combined with Data Fusion L. Bowman et al. https://doi.org/10.3390/s23218811
18. Multilevel Unsupervised Domain Adaptation for Single-Stage Object Detection in Remote Sensing Images S. Luo et al. https://doi.org/10.1109/JSTARS.2024.3479224
19. Birds of a Feather Flock Together: Category-Divergence Guidance for Domain Adaptive Segmentation B. Yuan et al. https://doi.org/10.1109/TIP.2022.3162471
20. 轻量化文本图像多模态遥感图像语义分割模型 王. Wang Zhiying & 汪. Wang Xili https://doi.org/10.3788/LOP250572
21. Spectral Clustering of Straight-Line Segments for Roof Plane Extraction From Airborne LiDAR Point Clouds C. Zhang et al. https://doi.org/10.1109/LGRS.2017.2785380
22. Weakly-Supervised Semantic Segmentation of ALS Point Clouds Based on Auxiliary Line and Plane Point Prediction J. Chen et al. https://doi.org/10.1109/JSTARS.2024.3469269
23. WCMNet: A Wavelet-Guided and CNN–Mamba Hybrid Network Approach for Unsupervised Domain Adaptation in Building Extraction D. Yang et al. https://doi.org/10.3390/rs18132265
24. 多层次分支跨尺度融合的遥感图像语义分割网络 曾. Zeng Junying et al. https://doi.org/10.3788/LOP241148
25. FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery Z. Zheng et al. https://doi.org/10.1109/TPAMI.2023.3296757
26. HRRF: a hierarchical recursive reasoning framework for high-resolution remote sensing semantic segmentation G. Zheng et al. https://doi.org/10.1080/10106049.2025.2583334
27. WHU-Stereo: A Challenging Benchmark for Stereo Matching of High-Resolution Satellite Images S. Li et al. https://doi.org/10.1109/TGRS.2023.3245205
28. Transferring Transformer-Based Models for Cross-Area Building Extraction From Remote Sensing Images C. Qiu et al. https://doi.org/10.1109/JSTARS.2022.3175200
29. Semantic Segmentation-Based Building Footprint Extraction Using Very High-Resolution Satellite Images and Multi-Source GIS Data W. Li et al. https://doi.org/10.3390/rs11040403
30. AFAD-Net: scene-adaptive diffusion model with Frequency-spatial logic for Remote Sensing Image Super-Resolution C. Jiang et al. https://doi.org/10.1080/01431161.2026.2735048
31. Geographical Knowledge-Driven Representation Learning for Remote Sensing Images W. Li et al. https://doi.org/10.1109/TGRS.2021.3115569
32. Pseudo Labeling Methods for Semi-Supervised Semantic Segmentation: A Review and Future Perspectives L. Ran et al. https://doi.org/10.1109/TCSVT.2024.3508768
33. A Comparison of Deep Learning Methods for Airborne Lidar Point Clouds Classification N. Li et al. https://doi.org/10.1109/JSTARS.2021.3091389
34. EyeNet++: A Multiscale and Multidensity Approach for Outdoor 3-D Semantic Segmentation Inspired by the Human Visual Field S. Yoo et al. https://doi.org/10.1109/TGRS.2025.3589287
35. Semisupervised Semantic Segmentation With Certainty-Aware Consistency Training for Remote Sensing Imagery Y. Guo et al. https://doi.org/10.1109/JSTARS.2023.3255553
36. Enhanced remote sensing image segmentation via dual-branch uncertainty-aware haar-enhanced network W. Shi et al. https://doi.org/10.1007/s10044-026-01685-0
37. TEMPORARY REMOVAL: Aerial imagery for roof segmentation: A large-scale dataset towards automatic mapping of buildings Q. Chen et al. https://doi.org/10.1016/j.isprsjprs.2018.11.011
38. Multiscale Feature Learning by Transformer for Building Extraction From Satellite Images X. Chen et al. https://doi.org/10.1109/LGRS.2022.3142279
39. A Deep Transfer Learning Framework Using Teacher–Student Structure for Land Cover Classification of Remote-Sensing Imagery X. Zhang et al. https://doi.org/10.1109/LGRS.2023.3312591
40. SegMind: Semisupervised Remote Sensing Image Semantic Segmentation With Masked Image Modeling and Contrastive Learning Method Z. Li et al. https://doi.org/10.1109/TGRS.2023.3321041
41. Using convolutional neural networks incorporating hierarchical active learning for target-searching in large-scale remote sensing images G. Xu et al. https://doi.org/10.1080/01431161.2020.1714774
42. An Aerial Photogrammetry Benchmark Dataset for Point Cloud Segmentation and Style Translation M. Chen et al. https://doi.org/10.3390/rs16224240
43. High-Resolution Remote Sensing Image Segmentation Based on Spatial-Frequency Feature Enhancement and Dual Attention Fusion J. Cao et al. https://doi.org/10.1109/ACCESS.2026.3727116
44. Unsupervised Building Instance Segmentation of Airborne LiDAR Point Clouds for Parallel Reconstruction Analysis Y. Zhang et al. https://doi.org/10.3390/rs13061136
45. Novel Half-Spaces Based 3D Building Reconstruction Using Airborne LiDAR Data M. Bizjak et al. https://doi.org/10.3390/rs15051269
46. TOV: The Original Vision Model for Optical Remote Sensing Image Understanding via Self-Supervised Learning C. Tao et al. https://doi.org/10.1109/JSTARS.2023.3271312
47. Large-scale building height retrieval from single SAR imagery based on bounding box regression networks Y. Sun et al. https://doi.org/10.1016/j.isprsjprs.2021.11.024
48. Deep learning in remote sensing applications: A meta-analysis and review L. Ma et al. https://doi.org/10.1016/j.isprsjprs.2019.04.015
49. Lossy Neural Compression for Geospatial Analytics: A review C. Gomes et al. https://doi.org/10.1109/MGRS.2025.3546527
50. AMM-FuseNet: Attention-Based Multi-Modal Image Fusion Network for Land Cover Mapping W. Ma et al. https://doi.org/10.3390/rs14184458
51. R3-Net: A Deep Network for Multioriented Vehicle Detection in Aerial Images and Videos Q. Li et al. https://doi.org/10.1109/TGRS.2019.2895362
52. Deep-Learning-Based 3-D Surface Reconstruction—A Survey A. Farshian et al. https://doi.org/10.1109/JPROC.2023.3321433
53. BCAFNet: A Bi-Directional Cross-Modal Attention Network With 2-D–3-D Consistency for Multimodal Point Cloud Semantic Segmentation H. Li et al. https://doi.org/10.1109/TGRS.2026.3714130
54. Semantic Segmentation of Remote Sensing Images With Self-Supervised Multitask Representation Learning W. Li et al. https://doi.org/10.1109/JSTARS.2021.3090418
55. Public Building Geometric Models From Point Clouds: A multidimensional quality evaluation framework D. Chen et al. https://doi.org/10.1109/MGRS.2025.3618468
56. SDFCNv2: An Improved FCN Framework for Remote Sensing Images Semantic Segmentation G. Chen et al. https://doi.org/10.3390/rs13234902
57. DF2RQ: Dynamic Feature Fusion via Region-Wise Queries for Semantic Segmentation of Multimodal Remote Sensing Data S. Feng et al. https://doi.org/10.1109/TGRS.2025.3526247
58. Enhancing the ability of convolutional neural networks for remote sensing image segmentation using transformers M. Barr https://doi.org/10.1007/s00521-024-09743-6
59. Tackling Dual Gaps in Remote Sensing Segmentation: Task-Oriented Super-Resolution for Domain Adaptation E. Hong et al. https://doi.org/10.1109/ACCESS.2024.3509984
60. Toward Stable Semi-Supervised Remote Sensing Segmentation via Co-Guidance and Co-Fusion Y. Zhou et al. https://doi.org/10.1109/TGRS.2026.3680315
61. Dynamic Inversion Method of Calculating Large-Scale Urban Building Height Based on Cooperative Satellite Laser Altimetry and Multi-Source Optical Remote Sensing H. Xia et al. https://doi.org/10.3390/land13081120
62. Semantic Segmentation of Satellite Images for Landslide Detection Using Foreground-Aware and Multi-Scale Convolutional Attention Mechanism C. Yu et al. https://doi.org/10.3390/s24206539
63. Classification of Urban Building Type from High Spatial Resolution Remote Sensing Imagery Using Extended MRS and Soft BP Network J. Xie & J. Zhou https://doi.org/10.1109/JSTARS.2017.2686422
64. Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images G. Chen et al. https://doi.org/10.1109/JSTARS.2018.2810320
65. There Are No Data Like More Data: Datasets for deep learning in Earth observation M. Schmitt et al. https://doi.org/10.1109/MGRS.2023.3293459
66. CFMNet: A Lightweight Backbone With Cooperative Feature Modeling for Remote Sensing Vision Tasks J. Chen et al. https://doi.org/10.1109/TGRS.2026.3714070
67. FALSE: False Negative Samples Aware Contrastive Learning for Semantic Segmentation of High-Resolution Remote Sensing Image Z. Zhang et al. https://doi.org/10.1109/LGRS.2022.3222836
68. Improved Deep Learning-Based Vehicle Detection for Urban Applications Using Remote Sensing Imagery M. Ragab et al. https://doi.org/10.3390/rs15194747
69. Classification of image matching point clouds over an urban area T. Tran et al. https://doi.org/10.1080/01431161.2018.1452069
70. A random forest classifier based on pixel comparison features for urban LiDAR data C. Wang et al. https://doi.org/10.1016/j.isprsjprs.2018.12.009
71. SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds Q. Hu et al. https://doi.org/10.1007/s11263-021-01554-9
72. Height-Assisted Semi-Supervised Building Footprint Extraction From Optical Remote Sensing Images W. Huang et al. https://doi.org/10.1109/TGRS.2025.3642212
73. Semantic Segmentation of Remote-Sensing Imagery Using Heterogeneous Big Data: International Society for Photogrammetry and Remote Sensing Potsdam and Cityscape Datasets A. Song & Y. Kim https://doi.org/10.3390/ijgi9100601
74. LiEAF-Net: A Lightweight Multiscale Elevation-Aware Fusion Network for Multimodal Semantic Segmentation of High-Resolution Remote Sensing Imagery F. Sultonov et al. https://doi.org/10.1109/LGRS.2026.3729955
75. Automatic Target Detection from Satellite Imagery Using Machine Learning A. Tahir et al. https://doi.org/10.3390/s22031147
76. Context-based automatic reconstruction and texturing of 3D urban terrain for quick-response tasks D. Bulatov et al. https://doi.org/10.1016/j.isprsjprs.2014.02.016
77. Comparative analysis of deep learning based building extraction methods with the new VHR Istanbul dataset T. Bakirman et al. https://doi.org/10.1016/j.eswa.2022.117346
78. Change detection on LOD 2 building models with very high resolution spaceborne stereo imagery R. Qin https://doi.org/10.1016/j.isprsjprs.2014.07.007
79. Multiscale Semantic Fusion-Guided Fractal Convolutional Object Detection Network for Optical Remote Sensing Imagery T. Zhang et al. https://doi.org/10.1109/TGRS.2021.3108476
80. A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation S. Sarker et al. https://doi.org/10.1007/s00138-024-01543-1
81. Automatic Detection and Segmentation of Columns in As-Built Buildings from Point Clouds L. Díaz-Vilariño et al. https://doi.org/10.3390/rs71115651
82. Airborne LiDAR Point Cloud Building Reconstruction Based on Planar Optimal Combination and Feature Line Constraints Z. Hai et al. https://doi.org/10.3390/ijgi15020092
83. Capturing Small Objects and Edges Information for Cross-Sensor and Cross-Region Land Cover Semantic Segmentation in Arid Areas P. Yuan et al. https://doi.org/10.1109/JSTARS.2022.3232409
84. Multiprototype Relational Network for Few-Shot ALS Point Cloud Semantic Segmentation by Transferring Knowledge From Photogrammetric Point Clouds M. Dai et al. https://doi.org/10.1109/TGRS.2024.3364181
85. Urban Building Classification (UBC) V2—A Benchmark for Global Building Detection and Fine-Grained Classification From Satellite Imagery X. Huang et al. https://doi.org/10.1109/TGRS.2023.3311093
86. Land Cover Classification Using SegNet with Slope, Aspect, and Multidirectional Shaded Relief Images Derived from Digital Surface Model D. Lee et al. https://doi.org/10.1155/2020/8825509
87. Energy-Based Models in Earth Observation: From Generation to Semisupervised Learning J. Castillo-Navarro et al. https://doi.org/10.1109/TGRS.2021.3126428
88. Self-Training-Based Unsupervised Domain Adaptation for Object Detection in Remote Sensing Imagery S. Luo et al. https://doi.org/10.1109/TGRS.2024.3457789
89. Scattered Mountainous Area Building Extraction From an Open Satellite Imagery Dataset S. Deng et al. https://doi.org/10.1109/LGRS.2023.3247620
90. DGFNet: Dual Gate Fusion Network for Land Cover Classification in Very High-Resolution Images Y. Guo et al. https://doi.org/10.3390/rs13183755
91. MultiVeg: A Very High-Resolution Benchmark for Deep Learning-Based Multi-Class Vegetation Segmentation C. Lee et al. https://doi.org/10.3390/rs18010028
92. SNLRUX++ for Building Extraction From High-Resolution Remote Sensing Images Y. Lei et al. https://doi.org/10.1109/JSTARS.2021.3135705
93. 3D reconstruction of building surface from airborne LiDAR point clouds based on improved structural constraints R. Zhao et al. https://doi.org/10.1080/01431161.2024.2365815
94. Accelerating Training Convergence for Point Cloud Semantic Segmentation of Large-Scale Urban Scenes With Scene-Ensemble Prototypes J. Han et al. https://doi.org/10.1109/TGRS.2026.3660139
95. An Automatic and Threshold-Free Performance Evaluation System for Building Extraction Techniques From Airborne LIDAR Data M. Awrangjeb & C. Fraser https://doi.org/10.1109/JSTARS.2014.2318694
96. RS-MTDF: Multiteacher Distillation and Fusion for Remote Sensing Semi-Supervised Semantic Segmentation J. Song et al. https://doi.org/10.1109/TGRS.2026.3703484
97. An Unsupervised, Open-Source Workflow for 2D and 3D Building Mapping From Airborne LiDAR Data H. Song & J. Jung https://doi.org/10.1109/JSTARS.2023.3329773
98. Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images M. Zhang et al. https://doi.org/10.3390/rs9050500
99. Domain adaptation method for extracting buildings from remote sensing images using dual contrastive learning C. He & X. Jing https://doi.org/10.1117/1.JRS.18.034517
100. SGMA: Semantic-Guided Modality-Aware Segmentation for Remote Sensing With Incomplete Multimodal Data L. Wen et al. https://doi.org/10.1109/TGRS.2026.3692798
101. Scene Compliant Trajectory Forecast With Agent-Centric Spatio-Temporal Grids D. Ridel et al. https://doi.org/10.1109/LRA.2020.2974393
102. A New Semantic Segmentation Method for Remote Sensing Images Integrating Coordinate Attention and SPD-Conv Z. Yang et al. https://doi.org/10.3390/sym15051037
103. DBFA-TSNet: A Three-Stage Building Extraction Network Based on Dual-Branch Fusion and Adaptive Enhancement J. Chen et al. https://doi.org/10.1109/TGRS.2025.3614250
104. Feature-based multimodal remote sensing image matching: Benchmark and state-of-the-art Z. Geng et al. https://doi.org/10.1016/j.isprsjprs.2025.08.028
105. Real-time segmentation of remote sensing images with a combination of clustering and Bayesian approaches Y. Song & J. Qu https://doi.org/10.1007/s11554-020-00990-z
106. MCPF-Net: Multi-Stage LiDAR-Image Collaborative Perception Fusion Network for Point Cloud Semantic Segmentation of Urban Scenes H. Li et al. https://doi.org/10.3390/rs18132218
107. EOVNet: Earth-Observation Image-Based Vehicle Detection Network Z. Gao et al. https://doi.org/10.1109/JSTARS.2019.2933501
108. Weakly-supervised domain adaptation for built-up region segmentation in aerial and satellite imagery J. Iqbal & M. Ali https://doi.org/10.1016/j.isprsjprs.2020.07.001
109. DPLG-Net: Deep Pseudo-Label-Guided Network for Weakly Supervised Semantic Segmentation of ALS Point Clouds J. Chen et al. https://doi.org/10.1109/TGRS.2026.3662731
110. A Class-Aware Semi-Supervised Framework for Semantic Segmentation of High-Resolution Remote Sensing Imagery S. Wang et al. https://doi.org/10.1109/JSTARS.2025.3601148
111. Towards zero-shot learning in 3D change detection: improving generalization with custom augmentations and evaluation R. Contu et al. https://doi.org/10.1080/22797254.2025.2453468
112. Spatial Reconstruction and Joint Training in Transformer Network for Cross-Domain Remote Sensing Images Semantic Segmentation J. Zeng et al. https://doi.org/10.1109/TGRS.2025.3599841
113. Building footprint extraction based on radiometric and geometric constraints in airborne oblique images J. Xiao & M. Gerke https://doi.org/10.1080/19479832.2015.1050073
114. An Improved MAE-Based Pretraining Method for Urban Public Space Monitoring With Optical Remote Sensing Imagery W. Wei et al. https://doi.org/10.1109/JSTARS.2025.3584546
115. FBSNet: Rethinking Directional Structure Modeling in Remote Sensing Segmentation via Frequency-Band State Space Networks Y. Li et al. https://doi.org/10.1109/TGRS.2026.3690600
116. GraNet: Global relation-aware attentional network for semantic segmentation of ALS point clouds R. Huang et al. https://doi.org/10.1016/j.isprsjprs.2021.04.017
117. True orthophoto generation using line segment matches Q. Wang et al. https://doi.org/10.1111/phor.12229
118. Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community J. Ball et al. https://doi.org/10.1117/1.JRS.11.042609
119. Object-Based Semi-Supervised Spatial Attention Residual UNet for Urban High-Resolution Remote Sensing Image Classification Y. Lu et al. https://doi.org/10.3390/rs16081444
120. HMSM-Net: Hierarchical multi-scale matching network for disparity estimation of high-resolution satellite stereo images S. He et al. https://doi.org/10.1016/j.isprsjprs.2022.04.020
121. Lidar Point Cloud Compression, Processing and Learning for Autonomous Driving R. Abbasi et al. https://doi.org/10.1109/TITS.2022.3167957
122. LuoJiaAI: A cloud-based artificial intelligence platform for remote sensing image interpretation Z. Zhang et al. https://doi.org/10.1080/10095020.2022.2162980
123. Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification M. Gerke & J. Xiao https://doi.org/10.1016/j.isprsjprs.2013.10.011
124. Building Outline Extraction Using a Heuristic Approach Based on Generalization of Line Segments T. Partovi et al. https://doi.org/10.1109/JSTARS.2016.2611861
125. Remote Sensing Tuning: A Survey D. Yin et al. https://doi.org/10.26599/CVM.2025.9450490
126. SRGT: Spatial-Resolution-Guided Teacher for Robust Semi-Supervised Aerial Object Detection Y. Li et al. https://doi.org/10.1109/TIP.2026.3724725
127. Building Corner Detection in Aerial Images with Fully Convolutional Networks W. Song et al. https://doi.org/10.3390/s19081915
128. Integrated BIM-GIS framework for holistic building stock assessment using 5D geo-modeling and digital twin concepts N. Elshaboury et al. https://doi.org/10.1016/j.jobe.2025.113391
129. ALPS: An Auto-Labeling and Pre-Training Scheme for Remote Sensing Segmentation With Segment Anything Model S. Zhang et al. https://doi.org/10.1109/TIP.2025.3556344
130. Morphological path filtering at the region scale for efficient and robust road network extraction from satellite imagery L. Courtrai & S. Lefèvre https://doi.org/10.1016/j.patrec.2016.05.014
131. Arbitrary-angle bounding box based location for object detection in remote sensing image F. Sun et al. https://doi.org/10.1080/22797254.2021.1880975
132. A Deep Neural Network Using Double Self-Attention Mechanism for ALS Point Cloud Segmentation L. Yu et al. https://doi.org/10.1109/ACCESS.2022.3158438
133. Learning Digital Terrain Models From Point Clouds: ALS2DTM Dataset and Rasterization-Based GAN H. Le et al. https://doi.org/10.1109/JSTARS.2022.3182030
134. Contextual classification of lidar data and building object detection in urban areas J. Niemeyer et al. https://doi.org/10.1016/j.isprsjprs.2013.11.001
135. UNet reimagined: advanced building extraction through dual attention and global-local synthesis Y. Lei et al. https://doi.org/10.1080/21642583.2025.2503238
136. Improving LiDAR classification accuracy by contextual label smoothing in post-processing N. Li et al. https://doi.org/10.1016/j.isprsjprs.2018.11.022
137. Improving Disparity Consistency With Self-Refined Cost Volumes for Deep Learning-Based Satellite Stereo Matching J. Kim et al. https://doi.org/10.1109/JSTARS.2025.3555424
138. Active Shapes for Automatic 3D Modeling of Buildings B. Sirmacek & R. Lindenbergh https://doi.org/10.3390/jimaging1010156
139. Classification of airborne 3D point clouds regarding separation of vegetation in complex environments D. Bulatov et al. https://doi.org/10.1364/AO.422973
140. Beyond image generation: visual data analysis with diffusion models—a comprehensive survey B. Olechno & J. Mańdziuk https://doi.org/10.1007/s10462-026-11615-5
141. Self-constructing graph neural networks to model long-range pixel dependencies for semantic segmentation of remote sensing images Q. Liu et al. https://doi.org/10.1080/01431161.2021.1936267
142. 3D Lidar Point Cloud Segmentation for Automated Driving R. Abbasi et al. https://doi.org/10.1109/MITS.2023.3325854
143. Large-Scale 3-D Building Reconstruction in LoD2 From ALS Point Clouds G. Kong et al. https://doi.org/10.1109/LGRS.2024.3514514
144. Confidence-Weighted Dual-Teacher Networks With Biased Contrastive Learning for Semi-Supervised Semantic Segmentation in Remote Sensing Images Y. Xin et al. https://doi.org/10.1109/TGRS.2024.3376352
145. LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban Areas Z. Ye et al. https://doi.org/10.3390/ijgi9070450
146. Integrating Gate and Attention Modules for High-Resolution Image Semantic Segmentation Z. Zheng et al. https://doi.org/10.1109/JSTARS.2021.3071353
147. ESDINet: Efficient Shallow-Deep Interaction Network for Semantic Segmentation of High-Resolution Aerial Images X. Zhang et al. https://doi.org/10.1109/TGRS.2024.3351437
148. A Dual-Branch Network Leveraging Heterogeneous Features for Semantic Segmentation of Large-Scale Point Clouds K. Liu et al. https://doi.org/10.1109/TGRS.2025.3641946
149. A new weakly supervised approach for ALS point cloud semantic segmentation P. Wang & W. Yao https://doi.org/10.1016/j.isprsjprs.2022.04.016
150. Global Context-Aware Point Cloud Semantic Segmentation Method Based on KPConv 明. 余 https://doi.org/10.12677/aam.2026.154149
151. IPCONV: Convolution with Multiple Different Kernels for Point Cloud Semantic Segmentation R. Zhang et al. https://doi.org/10.3390/rs15215136
152. Deep fusion of multi-view and multimodal representation of ALS point cloud for 3D terrain scene recognition N. Qin et al. https://doi.org/10.1016/j.isprsjprs.2018.03.011
153. Vision-Language Models in Remote Sensing: Current progress and future trends X. Li et al. https://doi.org/10.1109/MGRS.2024.3383473
154. A Large-Scale Aerial and Satellite Image Dataset for Deep Learning-Based Image Super-Resolution of Very High-Resolution Remote Sensing Imagery M. Chung et al. https://doi.org/10.1109/JSTARS.2025.3610081
155. Exploiting Remote Sensing Imagery for Vehicle Detection and Classification Using an Artificial Intelligence Technique M. Alajmi et al. https://doi.org/10.3390/rs15184600
156. Vehicle Instance Segmentation From Aerial Image and Video Using a Multitask Learning Residual Fully Convolutional Network L. Mou & X. Zhu https://doi.org/10.1109/TGRS.2018.2841808
157. Recent Advances and Perspectives in Deep Learning Techniques for 3D Point Cloud Data Processing Z. Ding et al. https://doi.org/10.3390/robotics12040100
158. A Multi-Step Fusion Network for Semantic Segmentation of High-Resolution Aerial Images Y. Yuan et al. https://doi.org/10.3390/s23115323
159. 一种耦合ResNet与Transformer的高分遥感语义分割方法 张. Zhang Lei et al. https://doi.org/10.3788/LOP250591
160. A DSM-Based Co-Occurrence Matrix for Semantic Classification W. Xia et al. https://doi.org/10.1109/LGRS.2020.3030401
161. A Remote Sensing Land Cover Classification Algorithm Based on Attention Mechanism X. Zhang et al. https://doi.org/10.1080/07038992.2021.1971961
162. Adaptive Multiscale Feature Extraction in a Distributed System for Semantic Classification of Airborne LiDAR Point Clouds S. Singh & J. Sreevalsan-Nair https://doi.org/10.1109/LGRS.2021.3099935
163. DALES Objects: A Large Scale Benchmark Dataset for Instance Segmentation in Aerial Lidar N. Singer & V. Asari https://doi.org/10.1109/ACCESS.2021.3094127
164. Automatic Building Extraction via Adaptive Iterative Segmentation With LiDAR Data and High Spatial Resolution Imagery Fusion S. Chen et al. https://doi.org/10.1109/JSTARS.2020.2992298
165. Autonomous Aerial Vehicles-Aided Intelligent Transportation Systems: Vision, Challenges, and Opportunities A. Telikani et al. https://doi.org/10.1109/COMST.2025.3530913
166. Elevation Estimation-Driven Building 3-D Reconstruction From Single-View Remote Sensing Imagery Y. Mao et al. https://doi.org/10.1109/TGRS.2023.3266477
167. UAV 3D Scene Understanding: A Survey from an Agent-Capability Evolution Perspective E. Zhu et al. https://doi.org/10.3390/rs18142323
168. Large-Scale Synthetic Urban Dataset for Aerial Scene Understanding Q. Gao et al. https://doi.org/10.1109/ACCESS.2020.2976686
169. Discrimination of residential and industrial buildings using LiDAR data and an effective spatial-neighbor algorithm in a typical urban industrial park L. Ma et al. https://doi.org/10.5721/EuJRS20154801
170. Diffusion Models and Pseudo-Change: A Transfer Learning-Based Change Detection in Remote Sensing Images J. Wang et al. https://doi.org/10.1109/TGRS.2024.3484526
171. Few-Shot Semantic Segmentation for Remote Sensing ALS Point Clouds via Multidimensional Geometric Feature Embedding H. Liu et al. https://doi.org/10.1109/JSTARS.2026.3708208
172. Transformer-based semantic segmentation for large-scale building footprint extraction from very-high resolution satellite images M. Gibril et al. https://doi.org/10.1016/j.asr.2024.03.002
173. Looking for a needle in a haystack: Probability density based classification and reconstruction of dormers from 3D point clouds Y. Dehbi et al. https://doi.org/10.1111/tgis.12658
174. MDAS: a new multimodal benchmark dataset for remote sensing J. Hu et al. https://doi.org/10.5194/essd-15-113-2023
175. Hidden Path Selection Network for Semantic Segmentation of Remote Sensing Images K. Yang et al. https://doi.org/10.1109/TGRS.2022.3197334
176. Translation-aware semantic segmentation via conditional least-square generative adversarial networks M. Zhang et al. https://doi.org/10.1117/1.JRS.11.042622
177. Large-Scale and Refined Green Space Identification-Based Sustainable Urban Renewal Mode Assessment R. Guo et al. https://doi.org/10.1155/2020/2043019
178. GraSS: Contrastive Learning With Gradient-Guided Sampling Strategy for Remote Sensing Image Semantic Segmentation Z. Zhang et al. https://doi.org/10.1109/TGRS.2023.3336285
179. Flexible building primitives for 3D building modeling B. Xiong et al. https://doi.org/10.1016/j.isprsjprs.2015.01.002
180. CTMU-Net: An Improved U-Net for Semantic Segmentation of Remote-Sensing Images Based on the Combined Attention Mechanism Y. Li et al. https://doi.org/10.1109/JSTARS.2023.3326960
181. 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
182. PANGAEA: Assessing Geospatial Foundation Models Capabilities through a Global and Inclusive Benchmark V. Marsocci et al. https://doi.org/10.1109/MGRS.2025.3628194
183. Dynamic Category Preference Learning: Tackling Long-Tailed Semi-Supervised Segmentation in Remote Sensing J. Li et al. https://doi.org/10.1109/TGRS.2025.3633770
184. Edge-Guided and Multiscale Feature Optimization-Based Semantic Segmentation Network for Remote Sensing Images C. Zhou et al. https://doi.org/10.1109/TGRS.2026.3660697
185. Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning R. Kemker et al. https://doi.org/10.1016/j.isprsjprs.2018.04.014
186. Dynamic and Adaptive Self-Training for Semi-Supervised Remote Sensing Image Semantic Segmentation J. Jin et al. https://doi.org/10.1109/TGRS.2024.3407142
187. WHU-Urban3D: An urban scene LiDAR point cloud dataset for semantic instance segmentation X. Han et al. https://doi.org/10.1016/j.isprsjprs.2024.02.007
188. PSODNet: Pretrained Scene-Aware Object Detection for Optical Remote Sensing Imagery H. Liu et al. https://doi.org/10.1109/TGRS.2025.3615623
189. -SIFT: SIFT feature extraction and matching for large images in large-scale aerial photogrammetry Y. Sun et al. https://doi.org/10.1016/j.isprsjprs.2014.02.001
190. IAMS (Interior-Anchored Mean-Shift) Algorithm for Supervoxel Segmentation of Airborne LiDAR Roof Points H. Zhou et al. https://doi.org/10.3390/rs18060965
191. Enhancing point cloud semantic segmentation in the data‐scarce domain of industrial plants through synthetic data F. Noichl et al. https://doi.org/10.1111/mice.13153
192. Remote Sensing-Based 3D Assessment of Landslides: A Review of the Data, Methods, and Applications H. Albanwan et al. https://doi.org/10.3390/rs16030455
193. DTAC-3DMesh: A 3-D Mesh Modeling Method for Buildings Using ArrayInSAR Point Clouds W. Duan et al. https://doi.org/10.1109/JSTARS.2026.3723368
194. Cross-modal remote sensing intelligent interpretation: method, data, and application K. FU et al. https://doi.org/10.1360/SSI-2023-0055
195. A Modeling and Measurement Approach for the Uncertainty of Features Extracted from Remote Sensing Images Q. Zhang et al. https://doi.org/10.3390/rs11161841
196. Unsupervised Single-Scene Semantic Segmentation for Earth Observation S. Saha et al. https://doi.org/10.1109/TGRS.2022.3174651
197. Semantic segmentation of 3D LiDAR data using deep learning: a review of projection-based methods A. Jhaldiyal & N. Chaudhary https://doi.org/10.1007/s10489-022-03930-5
198. Boundary-aware Graph Convolutional Network for Building Roof Detection from High-resolution Remote Sensed Imagery J. Du et al. https://doi.org/10.1007/s41064-025-00352-z
199. Arbitrary-Shaped Building Boundary-Aware Detection With Pixel Aggregation Network X. Jiang et al. https://doi.org/10.1109/JSTARS.2020.3017934
200. Globe230k: A Benchmark Dense-Pixel Annotation Dataset for Global Land Cover Mapping Q. Shi et al. https://doi.org/10.34133/remotesensing.0078
201. A global linear attention network for semantic segmentation of remote sensing images Y. Fang et al. https://doi.org/10.1016/j.knosys.2026.115625
202. Post‐filtering with surface orientation constraints for stereo dense image matching X. Huang & R. Qin https://doi.org/10.1111/phor.12333
203. RiSSNet: Contrastive Learning Network with a Relaxed Identity Sampling Strategy for Remote Sensing Image Semantic Segmentation H. Li et al. https://doi.org/10.3390/rs15133427
204. Robust point cloud classification based on multi-level semantic relationships for urban scenes Q. Zhu et al. https://doi.org/10.1016/j.isprsjprs.2017.04.022
205. Shape Activated CAM Learning for Weakly Supervised Remote Sensing Semantic Segmentation H. Chen et al. https://doi.org/10.1109/TGRS.2025.3578466
206. Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery S. Crommelinck et al. https://doi.org/10.3390/rs11212505
207. Deep generative modelling for building facade footprint extraction from ALS point clouds N. Wang et al. https://doi.org/10.1080/2150704X.2026.2721645
208. A review of neural architecture search methods for super-resolution imaging J. Guo et al. https://doi.org/10.1007/s10462-025-11488-0
209. Line matching based on planar homography for stereo aerial images Y. Sun et al. https://doi.org/10.1016/j.isprsjprs.2014.12.003
210. Multiscale Cascaded Network for the Semantic Segmentation of High-Resolution Remote Sensing Images X. Zhang et al. https://doi.org/10.1080/07038992.2023.2255068
211. Exploiting Superpixel-Based Contextual Information on Active Learning for High Spatial Resolution Remote Sensing Image Classification J. Tang et al. https://doi.org/10.3390/rs15030715
212. Deep built-structure counting in satellite imagery using attention based re-weighting A. Shakeel et al. https://doi.org/10.1016/j.isprsjprs.2019.03.014
213. A survey on the utilization of Superpixel image for clustering based image segmentation B. Sasmal & K. Dhal https://doi.org/10.1007/s11042-023-14861-9
214. An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment F. Wang et al. https://doi.org/10.3390/rs15092432
215. Decouple and weight semi-supervised semantic segmentation of remote sensing images W. Huang et al. https://doi.org/10.1016/j.isprsjprs.2024.04.010
216. MARE: Self-Supervised Multi-Attention REsu-Net for Semantic Segmentation in Remote Sensing V. Marsocci et al. https://doi.org/10.3390/rs13163275
217. Cross-Domain Incremental Feature Learning for ALS Point Cloud Semantic Segmentation With Few Samples M. Dai et al. https://doi.org/10.1109/TGRS.2024.3524212
218. Continual Barlow Twins: Continual Self-Supervised Learning for Remote Sensing Semantic Segmentation V. Marsocci & S. Scardapane https://doi.org/10.1109/JSTARS.2023.3280029
219. Multi-temporal urban semantic understanding based on GF-2 remote sensing imagery: from tri-temporal datasets to multi-task mapping S. Shi et al. https://doi.org/10.1080/17538947.2023.2246445
220. Advances and Future Prospects in Building Extraction From High-Resolution Remote Sensing Images D. Yang et al. https://doi.org/10.1109/JSTARS.2025.3538662
221. O-rDN: Mitigating Graph Attention Bias in Scene Parsing of Urban Remote Sensing Imagery J. Xu et al. https://doi.org/10.1109/ACCESS.2026.3715205
222. MSANet: an improved semantic segmentation method using multi-scale attention for remote sensing images X. Zhang et al. https://doi.org/10.1080/2150704X.2022.2142075
223. Bayesian U-Net: Estimating Uncertainty in Semantic Segmentation of Earth Observation Images C. Dechesne et al. https://doi.org/10.3390/rs13193836
224. CUS3D: A New Comprehensive Urban-Scale Semantic-Segmentation 3D Benchmark Dataset L. Gao et al. https://doi.org/10.3390/rs16061079
225. DiverseNet: Decision Diversified Semi-Supervised Semantic Segmentation Networks for Remote Sensing Imagery W. Ma et al. https://doi.org/10.1109/JSTARS.2025.3574229
226. Classification From Airborne Traditional Point Cloud to Multispectral Point Cloud: A Review Q. Wang et al. https://doi.org/10.1109/JSTARS.2026.3674157
227. Stereo Dense Image Matching by Adaptive Fusion of Multiple-Window Matching Results Y. Han et al. https://doi.org/10.3390/rs12193138
228. Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification Y. Mao et al. https://doi.org/10.1016/j.isprsjprs.2022.03.019
229. A Novel Multi-Scale Attention PFE-UNet for Forest Image Segmentation B. Zhang et al. https://doi.org/10.3390/f12070937
230. Unsupervised Domain Adaptation Semantic Segmentation of High-Resolution Remote Sensing Imagery With Invariant Domain-Level Prototype Memory J. Zhu et al. https://doi.org/10.1109/TGRS.2023.3243042
231. Geographical Supervision Correction for Remote Sensing Representation Learning W. Li et al. https://doi.org/10.1109/TGRS.2022.3202499
232. An Open Benchmark Dataset for Forest Characterization from Sentinel-1 and -2 Time Series S. Hauser et al. https://doi.org/10.3390/rs16030488
233. M2FE-YOLO: Multibranch and Multilevel Feature Enhancement Network for Remote Sensing Object Detection Q. Wu et al. https://doi.org/10.1109/TGRS.2025.3612212
234. Geometric Positioning Verification of Spaceborne Photon-Counting Lidar Data Based on Terrain Feature Identification C. Wu et al. https://doi.org/10.1109/JSTARS.2024.3479315
235. Spatial structure-aware and cross-scale feature modeling network for remote sensing image semantic segmentation F. Huang & Y. Guo https://doi.org/10.3934/era.2025282
236. Global and Neighbor-Aware Token Learning for Weakly Supervised Remote Sensing Image Semantic Segmentation M. Gu et al. https://doi.org/10.1109/TGRS.2026.3724609
237. Asymmetric Cascade Fusion Network for Building Extraction S. Chan et al. https://doi.org/10.1109/TGRS.2023.3306018
238. ClassHyPer: ClassMix-Based Hybrid Perturbations for Deep Semi-Supervised Semantic Segmentation of Remote Sensing Imagery Y. He et al. https://doi.org/10.3390/rs14040879
239. Improved Faster R-CNN With Multiscale Feature Fusion and Homography Augmentation for Vehicle Detection in Remote Sensing Images H. Ji et al. https://doi.org/10.1109/LGRS.2019.2909541
240. The Eyes of the Gods: A Survey of Unsupervised Domain Adaptation Methods Based on Remote Sensing Data M. Xu et al. https://doi.org/10.3390/rs14174380
241. CDEST: Class Distinguishability-Enhanced Self-Training Method for Adopting Pre-Trained Models to Downstream Remote Sensing Image Semantic Segmentation M. Zhang et al. https://doi.org/10.3390/rs16071293
242. EnhancedNet, an End-to-End Network for Dense Disparity Estimation and its Application to Aerial Images J. Kang et al. https://doi.org/10.1007/s41064-024-00307-w
243. CSF-Net: Color Spectrum Fusion Network for Semantic Labeling of Airborne Laser Scanning Point Cloud J. Li et al. https://doi.org/10.1109/JSTARS.2021.3133602
244. Deep-Learning-Based Semantic Segmentation for Remote Sensing: A Bibliometric Literature Review K. Hasan et al. https://doi.org/10.1109/JSTARS.2023.3328315
245. DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing G. Lenczner et al. https://doi.org/10.1109/JSTARS.2022.3166551
246. Advancements in point cloud-based 3D defect classification and segmentation for industrial systems: A comprehensive survey A. Rani et al. https://doi.org/10.1016/j.inffus.2024.102575
247. Integrating Spatial Context With Generative Model for Building Reconstruction From Airborne LiDAR Point Clouds Y. Zhao et al. https://doi.org/10.1109/TGRS.2026.3670027
248. SMART SURVEILLANCE: ADVANCED DEEP LEARNING-BASED VEHICLE DETECTION AND TRACKING MODEL ON UAV IMAGERY K. OTHMAN et al. https://doi.org/10.1142/S0218348X25400274
249. Three-Dimensional Reconstruction of Building Roofs from Airborne LiDAR Data Based on a Layer Connection and Smoothness Strategy Y. Wang et al. https://doi.org/10.3390/rs8050415
250. OccuPoint2Roof: A Progressive Joint Completion and Reconstruction Framework for 3D Building Roofs from Occluded LiDAR Point Clouds H. Zhang et al. https://doi.org/10.1016/j.jfranklin.2026.109128
251. Automated 3D Scene Reconstruction from Open Geospatial Data Sources: Airborne Laser Scanning and a 2D Topographic Database L. Zhu et al. https://doi.org/10.3390/rs70606710
252. Small Object Detection in Remote Sensing Images with Residual Feature Aggregation-Based Super-Resolution and Object Detector Network S. Bashir & Y. Wang https://doi.org/10.3390/rs13091854
253. From Attention Stacking to Architecture Rethinking: Dual-Path Decoupling for Efficient Remote Sensing Image Segmentation Z. Zhang et al. https://doi.org/10.1109/LGRS.2026.3672712
254. Parameter-Free Half-Spaces Based 3D Building Reconstruction Using Ground and Segmented Building Points from Airborne LiDAR Data with 2D Outlines M. Bizjak et al. https://doi.org/10.3390/rs13214430
255. A Label-Constraint Building Roof Detection Method From Airborne LiDAR Point Clouds J. Yang et al. https://doi.org/10.1109/LGRS.2020.2999818
256. RS-Dseg: semantic segmentation of high-resolution remote sensing images based on a diffusion model component with unsupervised pretraining Z. Luo et al. https://doi.org/10.1038/s41598-024-69022-1
257. ND-Net: Neighborhood Description Network for Large-scale Point Cloud Semantic Segmentation W. Zhou et al. https://doi.org/10.1109/JSTARS.2026.3699958
258. Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation H. Ning et al. https://doi.org/10.1080/19475683.2020.1803402
259. EXPLOITING REMOTE SENSING IMAGERY FOR VEHICLE DETECTION AND CLASSIFICATION USING ROBUST COMPETITIVE ALGORITHM WITH DEEP CONVOLUTIONAL NEURAL NETWORK K. OTHMAN et al. https://doi.org/10.1142/S0218348X25400031
260. An intelligent water drop algorithm with deep learning driven vehicle detection and classification T. Vaiyapuri et al. https://doi.org/10.3934/math.2024557
261. Inherit With Distillation and Evolve With Contrast: Exploring Class Incremental Semantic Segmentation Without Exemplar Memory D. Zhao et al. https://doi.org/10.1109/TPAMI.2023.3273574
262. Super resolution of laser range data based on image-guided fusion and dense matching X. Huang et al. https://doi.org/10.1016/j.isprsjprs.2018.07.001
263. Pretrain a Remote Sensing Foundation Model by Promoting Intra-Instance Similarity X. An et al. https://doi.org/10.1109/TGRS.2024.3469568
264. Enhancing satellite semantic maps with ground-level imagery V. Balaska et al. https://doi.org/10.1016/j.robot.2021.103760
265. Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images N. Audebert et al. https://doi.org/10.3390/rs9040368
266. CrossMatch: Cross-View Matching for Semi-Supervised Remote Sensing Image Segmentation R. Liu et al. https://doi.org/10.1109/TGRS.2024.3507050
267. Results of the ISPRS benchmark on urban object detection and 3D building reconstruction F. Rottensteiner et al. https://doi.org/10.1016/j.isprsjprs.2013.10.004
268. Cycle graph analysis for 3D roof structure modelling: Concepts and performance G. Perera & H. Maas https://doi.org/10.1016/j.isprsjprs.2014.04.017
269. RePIC-Seg: Reciprocal Prototype–Image Consistency-Driven Semantic Segmentation for Remote Sensing Images H. Zhu et al. https://doi.org/10.1109/LGRS.2026.3673074
270. DE-Net: Deep Encoding Network for Building Extraction from High-Resolution Remote Sensing Imagery H. Liu et al. https://doi.org/10.3390/rs11202380
271. Robust Normal Estimation for 3D LiDAR Point Clouds in Urban Environments R. Zhao et al. https://doi.org/10.3390/s19051248
272. Deep Learning for 3D Point Clouds: A Survey Y. Guo et al. https://doi.org/10.1109/TPAMI.2020.3005434
273. Cropland Extraction Based on PlanetScope Images and a Newly Developed CAFM-Net Model J. Ren et al. https://doi.org/10.3390/rs18040646
274. Deep Attention and Multi-Scale Networks for Accurate Remote Sensing Image Segmentation X. Qi et al. https://doi.org/10.1109/ACCESS.2020.3015587
275. Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks Q. Liu et al. https://doi.org/10.1080/01431161.2022.2098078
276. City-scale building instance segmentation from LiDAR point clouds via structure-aware method J. Li et al. https://doi.org/10.1016/j.jag.2026.105086
277. Semantic Segmentation With Attention Mechanism for Remote Sensing Images Q. Zhao et al. https://doi.org/10.1109/TGRS.2021.3085889
278. Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images E. Sertel et al. https://doi.org/10.3390/rs14184558
279. PIF-Net: A Deep Point-Image Fusion Network for Multimodality Semantic Segmentation of Very High-Resolution Imagery and Aerial Point Cloud Z. Guo et al. https://doi.org/10.1109/TGRS.2023.3342477
280. Spatialising uncertainty in image segmentation using weakly supervised convolutional neural networks: a case study from historical map processing J. Uhl et al. https://doi.org/10.1049/iet-ipr.2018.5484
281. Features, Color Spaces, and Boosting: New Insights on Semantic Classification of Remote Sensing Images P. Tokarczyk et al. https://doi.org/10.1109/TGRS.2014.2321423
282. DSCNet: a dual-branch path aggregation network for vegetation segmentation in mining-area remote sensing images Y. Wang et al. https://doi.org/10.1088/1361-6501/aea88a
283. Hierarchical Transfer Learning with Transformers to Improve Semantic Segmentation in Remote Sensing Land Use M. Chen & L. Li https://doi.org/10.3390/rs17020290
284. Vegetation Land Use/Land Cover Extraction From High-Resolution Satellite Images Based on Adaptive Context Inference Z. Zhan et al. https://doi.org/10.1109/ACCESS.2020.2969812
285. FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery X. Sun et al. https://doi.org/10.1016/j.isprsjprs.2021.12.004
286. Neural-logic multi-agent system for flood event detection A. Rafanelli et al. https://doi.org/10.3233/IA-230004
287. Continual Self-Supervised Learning With Masked Autoencoders in Remote Sensing L. Möllenbrok et al. https://doi.org/10.1109/LGRS.2025.3579585
288. SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection J. Qi et al. https://doi.org/10.1109/JSTARS.2025.3621474
289. Small Object Detection in Remote Sensing Images Based on Super-Resolution with Auxiliary Generative Adversarial Networks L. Courtrai et al. https://doi.org/10.3390/rs12193152
290. Masked Topology Convolutional Network for Classification and Segmentation of Remote Sensing Images F. Wang et al. https://doi.org/10.1109/TGRS.2024.3374097
291. Building Extraction from Airborne Laser Scanning Data: An Analysis of the State of the Art I. Tomljenovic et al. https://doi.org/10.3390/rs70403826
292. A Hierarchical Building Detection Method for Very High Resolution Remotely Sensed Images Combined with DSM Using Graph Cut Optimization R. Qin & W. Fang https://doi.org/10.14358/PERS.80.9.873
293. Class Imbalance in the Automatic Interpretation of Remote Sensing Images: A Review P. Chen et al. https://doi.org/10.1109/JSTARS.2025.3555567
294. Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs M. Awrangjeb & C. Fraser https://doi.org/10.3390/rs6053716
295. Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works C. Tao et al. https://doi.org/10.1109/TGRS.2023.3276853
296. Research on Multi-Stage Optimization for High-Precision Digital Surface Model and True Digital Orthophoto Map Generation Methods Y. Ge et al. https://doi.org/10.3390/rs18020197
297. MoViM: A Hybrid CNN Vision Mamba Network for Lightweight Semantic Segmentation of Multimodal Remote Sensing Images W. Du et al. https://doi.org/10.1109/LGRS.2025.3623603