SEN12MS – A CURATED DATASET OF GEOREFERENCED MULTI-SPECTRAL SENTINEL-1/2 IMAGERY FOR DEEP LEARNING AND DATA FUSION

219 citations as recorded by crossref.

1. TimeSen2Crop: A Million Labeled Samples Dataset of Sentinel 2 Image Time Series for Crop-Type Classification G. Weikmann et al. https://doi.org/10.1109/JSTARS.2021.3073965
2. SwiMDiff: Scene-Wide Matching Contrastive Learning With Diffusion Constraint for Remote Sensing Image J. Tian et al. https://doi.org/10.1109/TGRS.2024.3371481
3. FL-SPDP: Spatially Modulated Differentially Private Federated Learning for Robust Satellite Image Recognition Z. Yang et al. https://doi.org/10.3390/electronics15030663
4. Multi-Scale Feature Fusion Network with Symmetric Attention for Land Cover Classification Using SAR and Optical Images D. Xu et al. https://doi.org/10.3390/rs16060957
5. Report on the 2020 IEEE GRSS Data Fusion Contest-Global Land Cover Mapping With Weak Supervision [Technical Committees] N. Yokoya et al. https://doi.org/10.1109/MGRS.2020.3033515
6. Forensic research of satellite images forgery: a comprehensive survey X. Ding et al. https://doi.org/10.1007/s10462-024-10909-w
7. Forest Disturbance Detection via Self-Supervised and Transfer Learning With Sentinel-1&2 Images R. Kuzu et al. https://doi.org/10.1109/JSTARS.2024.3361183
8. Clean Collector Algorithm for Satellite Image Pre-Processing of SAR-to-EO Translation M. Kim et al. https://doi.org/10.3390/electronics13224529
9. Recent Advancements in Deep Learning for SAR-to-Optical Image Translation: A Systematic Literature Review (2021–2025) T. Dove et al. https://doi.org/10.1109/JSTARS.2026.3682953
10. LM-Net: A Lightweight Matching Network for Remote Sensing Image Matching and Registration D. Quan et al. https://doi.org/10.1109/TGRS.2024.3509638
11. S3OIL: Semi-Supervised SAR-to-Optical Image Translation via Multi-Scale and Cross-Set Matching X. Yang et al. https://doi.org/10.1109/TIP.2025.3616576
12. A Diffusion-Guided Task Decomposition Framework for SAR-to-Optical Image Translation D. Du et al. https://doi.org/10.1109/JSTARS.2025.3623105
13. SSL4EO-S12: A large-scale multimodal, multitemporal dataset for self-supervised learning in Earth observation [Software and Data Sets] Y. Wang et al. https://doi.org/10.1109/MGRS.2023.3281651
14. An Integrated Pixel-Level Reflectance Adjustment (IPRA) for Harmonizing GF-1/6 WFV and Sentinel-2 MSI Data J. Shi et al. https://doi.org/10.3390/s26061759
15. High-Resolution Land Cover Mapping Through Learning With Noise Correction R. Dong et al. https://doi.org/10.1109/TGRS.2021.3068280
16. Self-Supervised Learning for Invariant Representations From Multi-Spectral and SAR Images P. Jain et al. https://doi.org/10.1109/JSTARS.2022.3204888
17. Foundation Models in Remote Sensing: Evolving from unimodality to multimodality D. Hong et al. https://doi.org/10.1109/MGRS.2026.3669086
18. Multilevel Feature Fusion-Based CNN for Local Climate Zone Classification From Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset C. Qiu et al. https://doi.org/10.1109/JSTARS.2020.2995711
19. A2-MAE: A Spatial–Temporal–Spectral Unified Remote Sensing Pretraining Method Based on Anchor-Aware Masked Autoencoder L. Zhang et al. https://doi.org/10.1109/TGRS.2026.3664662
20. Satellite SAR-to-optical translation with pre-trained large-scale generative models J. Wei et al. https://doi.org/10.1080/2150704X.2026.2684278
21. A global multimodal flood event dataset with heterogeneous text and multi-source remote sensing images Z. Zhang et al. https://doi.org/10.1080/20964471.2024.2358615
22. 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
23. Towards global flood mapping onboard low cost satellites with machine learning G. Mateo-Garcia et al. https://doi.org/10.1038/s41598-021-86650-z
24. Removing Cloud Cover Interference from Sentinel-2 imagery in Google Earth Engine by fusing Sentinel-1 SAR data with a CNN model X. Zhang et al. https://doi.org/10.1080/01431161.2021.2012295
25. Graph-Based Propagation for Multispectral Remote Sensing Image Completion I. Rolland et al. https://doi.org/10.1109/TGRS.2025.3527056
26. Pilot-Site Land Cover Mapping Using an Externally-Guided Clustering Framework: A Case Study from Ontario, Canada S. Omar et al. https://doi.org/10.3390/geomatics6040077
27. Benchmarking and scaling of deep learning models for land cover image classification I. Papoutsis et al. https://doi.org/10.1016/j.isprsjprs.2022.11.012
28. Multisensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery P. Ebel et al. https://doi.org/10.1109/TGRS.2020.3024744
29. A Deep Learning Approach to an Enhanced Building Footprint and Road Detection in High-Resolution Satellite Imagery C. Ayala et al. https://doi.org/10.3390/rs13163135
30. MSSI: a multi-scale shift-invariant metric learning network for visible and long-wave infrared image patch matching X. Zhang et al. https://doi.org/10.1080/01431161.2025.2549102
31. Distilled Heterogeneous Feature Alignment Network for SAR Image Semantic Segmentation M. Gao et al. https://doi.org/10.1109/LGRS.2023.3293160
32. Deep learning and traditional methods for remote sensing image analysis S. Rao et al. https://doi.org/10.1007/s44352-026-00027-4
33. Deep-Learning-Based Semantic Segmentation of Remote Sensing Images: A Survey L. Huang et al. https://doi.org/10.1109/JSTARS.2023.3335891
34. Holistic Response Lifting for Weakly Supervised Land-Cover Classification Q. Ma et al. https://doi.org/10.1109/TGRS.2025.3609534
35. Multimodal arbitrary-scale super-resolution via dynamic gated fusion and low-resolution semantic guidance H. Jin & Y. Deng https://doi.org/10.1007/s00371-026-04466-2
36. Learning-Based Near-Infrared Band Simulation with Applications on Large-Scale Landcover Classification X. Yuan et al. https://doi.org/10.3390/s23094179
37. GFTT: Geographical Feature Tokenization Transformer for SAR-to-Optical Image Translation H. Liang et al. https://doi.org/10.1109/JSTARS.2024.3523274
38. FG-GAN: A Fine-Grained Generative Adversarial Network for Unsupervised SAR-to-Optical Image Translation X. Yang et al. https://doi.org/10.1109/TGRS.2022.3165371
39. Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion A. Meraner et al. https://doi.org/10.1016/j.isprsjprs.2020.05.013
40. Multi-Relation Attention Network for Image Patch Matching D. Quan et al. https://doi.org/10.1109/TIP.2021.3101414
41. TransFusion-CR: Two-Phase SAR-to-Optical Translation and Deep Feature Fusion for Cloud Removal R. Liu et al. https://doi.org/10.1109/TGRS.2024.3439854
42. Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience: A Survey M. Savelonas et al. https://doi.org/10.3390/rs14236017
43. S1S2-Water: A Global Dataset for Semantic Segmentation of Water Bodies From Sentinel- 1 and Sentinel-2 Satellite Images M. Wieland et al. https://doi.org/10.1109/JSTARS.2023.3333969
44. A Weakly Supervised Semantic Segmentation Framework for Medium-Resolution Forest Classification With Noisy Labels and GF-1 WFV Images X. Peng et al. https://doi.org/10.1109/TGRS.2024.3404953
45. Fine-Grained Hashing for High Spatial Resolution Optical-SAR Remote Sensing Image Retrieval G. Lingyun et al. https://doi.org/10.1109/JSTARS.2026.3653578
46. Relational representation learning network for cross-spectral image patch matching C. Yu et al. https://doi.org/10.1016/j.inffus.2025.103749
47. A Multi-Level Cross-Modal Edge Filtering Method for High-Resolution Optical-SAR Image Registration J. Lan et al. https://doi.org/10.3390/rs18111741
48. Semi-Supervised Learning for Joint SAR and Multispectral Land Cover Classification A. Montanaro et al. https://doi.org/10.1109/LGRS.2022.3195259
49. Land Use and Land Cover Classification Meets Deep Learning: A Review S. Zhao et al. https://doi.org/10.3390/s23218966
50. Land Use and Land Cover Classification with Deep Learning-Based Fusion of SAR and Optical Data A. Irfan et al. https://doi.org/10.3390/rs17071298
51. SegGen: An Unreal Engine 5 Pipeline for Generating Multimodal Semantic Segmentation Datasets J. McMillen & Y. Yilmaz https://doi.org/10.3390/s25175569
52. MLRSNet: A multi-label high spatial resolution remote sensing dataset for semantic scene understanding X. Qi et al. https://doi.org/10.1016/j.isprsjprs.2020.09.020
53. Vision Foundation Models in Remote Sensing: A survey S. Lu et al. https://doi.org/10.1109/MGRS.2025.3541952
54. Explaining the Effects of Clouds on Remote Sensing Scene Classification J. Gawlikowski et al. https://doi.org/10.1109/JSTARS.2022.3221788
55. Semantic Segmentation of Remotely Sensed Images for Land-use and Land-cover Classification: A Comprehensive Review A. Putty et al. https://doi.org/10.1080/02564602.2025.2485918
56. 3DMAE: Joint SAR and Optical Representation Learning With Vertical Masking L. Zhang et al. https://doi.org/10.1109/LGRS.2023.3316193
57. 3MOS: a multi-source, multi-resolution, and multi-scene optical-SAR dataset with insights for multi-modal image matching Y. Ye et al. https://doi.org/10.1007/s44267-025-00091-0
58. Adaptive fuzzy convolution networks for uncertainty-aware image analysis in ambiguous environments S. Iqbal et al. https://doi.org/10.1016/j.eswa.2025.128407
59. Self-Distillation Feature Learning Network for Optical and SAR Image Registration D. Quan et al. https://doi.org/10.1109/TGRS.2022.3173476
60. Automatic extraction of land cover statistics from satellite imagery by deep learning E. Bernasconi et al. https://doi.org/10.3233/SJI-190555
61. Generative Artificial Intelligence Meets Synthetic Aperture Radar: A survey Z. Huang et al. https://doi.org/10.1109/MGRS.2024.3483459
62. Google Earth Engine: A Global Analysis and Future Trends A. Velastegui-Montoya et al. https://doi.org/10.3390/rs15143675
63. HieraRS: A Hierarchical Segmentation Paradigm for Remote Sensing Enabling Multigranularity Interpretation and Cross-Domain Transfer T. Ai et al. https://doi.org/10.1109/TGRS.2026.3673962
64. Interband Retrieval and Classification Using the Multilabeled Sentinel-2 BigEarthNet Archive U. Chaudhuri et al. https://doi.org/10.1109/JSTARS.2021.3112209
65. Manifold embeddings achieve comparable performance with multispectral imagery for time-series based land disturbance detection M. Li et al. https://doi.org/10.1080/17538947.2025.2523481
66. MSSDF: Modality-shared self-supervised distillation for high-resolution multi-modal remote sensing image learning T. Wang et al. https://doi.org/10.1016/j.inffus.2025.104006
67. Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning A. Ciocarlan & A. Stoian https://doi.org/10.3390/rs13214255
68. A Review of Optical and SAR Image Deep Feature Fusion in Semantic Segmentation C. Liu et al. https://doi.org/10.1109/JSTARS.2024.3424831
69. Addressing Non-Co-Visible Regions in SAR and Optical Image Registration: A Two-Stage Matching and Ensemble Method B. Ding & G. Yang https://doi.org/10.1109/TAES.2024.3493863
70. SARBA-Net: a boundary awareness network for impervious surface extraction of SAR images Y. Ding et al. https://doi.org/10.1080/10095020.2026.2695533
71. DiverseNet: Decision Diversified Semi-Supervised Semantic Segmentation Networks for Remote Sensing Imagery W. Ma et al. https://doi.org/10.1109/JSTARS.2025.3574229
72. Translation from dual-polarization complex-valued SAR to optical satellite images P. Ke et al. https://doi.org/10.1080/22797254.2026.2699356
73. Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images Z. Chen et al. https://doi.org/10.1109/JSTARS.2021.3053603
74. SEN12MS-CR-TS: A Remote-Sensing Data Set for Multimodal Multitemporal Cloud Removal P. Ebel et al. https://doi.org/10.1109/TGRS.2022.3146246
75. Deep Species Distribution Modeling From Sentinel-2 Image Time-Series: A Global Scale Analysis on the Orchid Family J. Estopinan et al. https://doi.org/10.3389/fpls.2022.839327
76. A semantic-enhanced multi-modal remote sensing foundation model for Earth observation K. Wu et al. https://doi.org/10.1038/s42256-025-01078-8
77. Learning SAR-to-Optical Image Translation via Diffusion Models With Color Memory Z. Guo et al. https://doi.org/10.1109/JSTARS.2024.3439516
78. Beyond Supervised Learning in Remote Sensing: A Systematic Review of Deep Learning Approaches B. Hosseiny et al. https://doi.org/10.1109/JSTARS.2023.3316733
79. From Orbit to Ground: A comprehensive review of multimodal self-supervised learning for remote sensing L. Bai et al. https://doi.org/10.1109/MGRS.2025.3588505
80. CrtNet: A Cross-Model Residual Transformer Network for Structure-Guided Remote Sensing Scene Classification C. Chen et al. https://doi.org/10.3390/electronics15071366
81. Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring M. Tom et al. https://doi.org/10.1109/TGRS.2022.3211184
82. Deep Learning for Satellite‐Based Forest Disturbance Monitoring: Recent Advances and Challenges C. Natel et al. https://doi.org/10.1002/widm.70096
83. When Remote Sensing Meets Foundation Model: A Survey and Beyond C. Huo et al. https://doi.org/10.3390/rs17020179
84. FLoRA: Fusion-Latent for Optical Reconstruction and Flood Area Segmentation via Cross-Modal Multitask Distillation Network J. Talreja et al. https://doi.org/10.1109/TGRS.2026.3701300
85. Global Land-Cover Mapping With Weak Supervision: Outcome of the 2020 IEEE GRSS Data Fusion Contest C. Robinson et al. https://doi.org/10.1109/JSTARS.2021.3063849
86. Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities L. Zhang & L. Zhang https://doi.org/10.1109/MGRS.2022.3145854
87. U-Net feature fusion for multi-class semantic segmentation of urban fabrics from Sentinel-2 imagery: an application on Grand Est Region, France R. Wenger et al. https://doi.org/10.1080/01431161.2022.2054295
88. DACIA5: a Sentinel-1 and Sentinel-2 dataset for agricultural crop identification applications A. Băicoianu et al. https://doi.org/10.1080/20964471.2025.2512685
89. Multimodal Supervised Contrastive Learning in Remote Sensing Downstream Tasks P. Berg et al. https://doi.org/10.1109/LGRS.2024.3385995
90. Experience in mapping erode soils based on remote sensing data A. Achasov et al. https://doi.org/10.26565/1992-4224-2026-45-01
91. 2020 IEEE GRSS Data Fusion Contest: Global Land Cover Mapping With Weak Supervision [Technical Committees] N. Yokoya et al. https://doi.org/10.1109/MGRS.2020.2970124
92. OpenForest: a data catalog for machine learning in forest monitoring A. Ouaknine et al. https://doi.org/10.1017/eds.2024.53
93. A Globally Applicable Method for NDVI Estimation from Sentinel-1 SAR Backscatter Using a Deep Neural Network and the SEN12TP Dataset T. Roßberg & M. Schmitt https://doi.org/10.1007/s41064-023-00238-y
94. D-PolTransGAN: Scattering Guided Hybrid Transformer-GAN for SAR-Optical Image Translation Using Dual Polarization SAR J. Talreja et al. https://doi.org/10.1109/JSTARS.2026.3691677
95. MUMUCD: A Multimodal Multiclass Change Detection Dataset F. Serva et al. https://doi.org/10.1109/LGRS.2025.3585797
96. On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID Y. Long et al. https://doi.org/10.1109/JSTARS.2021.3070368
97. Deep Learning Methods for SAR and Optical Image Fusion: A Review C. Guo et al. https://doi.org/10.3390/rs18081196
98. CERMF-Net: A SAR-Optical Feature Fusion for Cloud Elimination From Sentinel-2 Imagery Using Residual Multiscale Dilated Network J. Anandakrishnan et al. https://doi.org/10.1109/JSTARS.2024.3411032
99. EarthNets: Empowering artificial intelligence for Earth observation Z. Xiong et al. https://doi.org/10.1109/MGRS.2024.3466998
100. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing T. Kattenborn et al. https://doi.org/10.1016/j.isprsjprs.2020.12.010
101. deepNIR: Datasets for Generating Synthetic NIR Images and Improved Fruit Detection System Using Deep Learning Techniques I. Sa et al. https://doi.org/10.3390/s22134721
102. Satellite data reconstruction under cloud cover for corn yield forecasting via multimodal fusion С. Балук & В. Антонів https://doi.org/10.36930/40360308
103. A multi-modal dataset and A multi-task network integrating SAR interferometric data for semantic segmentation and height estimation Z. Wu et al. https://doi.org/10.1016/j.isprsjprs.2026.06.021
104. An Overview on the Generation and Detection of Synthetic and Manipulated Satellite Images L. Abady et al. https://doi.org/10.1561/116.00000142
105. AmazonCRIME: un conjunto de datos y punto de referencia de Inteligencia Artificial Geoespacial para la clasificación de áreas potenciales vinculadas a Crímenes Ambientales Transnacionales en la Selva Amazónica J. Pinto-Hidalgo & J. Silva-Centeno https://doi.org/10.4995/raet.2022.15710
106. Advancing horizons in remote sensing: a comprehensive survey of deep learning models and applications in image classification and beyond S. Paheding et al. https://doi.org/10.1007/s00521-024-10165-7
107. AIR-POLSAR-CR1.0: A Benchmark Dataset for Cloud Removal in High-Resolution Optical Remote Sensing Images with Fully Polarized SAR Y. Wang et al. https://doi.org/10.3390/rs17020275
108. Classification and Feature Extraction Algorithm for On-Orbit Spacecraft Based on Optical–SAR Multimodal Information B. Jie et al. https://doi.org/10.1109/TAES.2025.3641558
109. Contrastive Self-Supervised Learning for Globally Distributed Landslide Detection O. Ghorbanzadeh et al. https://doi.org/10.1109/ACCESS.2024.3449447
110. 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
111. Deep Image Prior Amplitude SAR Image Anonymization E. Cannas et al. https://doi.org/10.3390/rs15153750
112. Better, Not Just More: Data-centric machine learning for Earth observation R. Roscher et al. https://doi.org/10.1109/MGRS.2025.3551556
113. NaSC-TG2: Natural Scene Classification With Tiangong-2 Remotely Sensed Imagery Z. Zhou et al. https://doi.org/10.1109/JSTARS.2021.3063096
114. Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing L. Yang et al. https://doi.org/10.3390/s22062416
115. Bare Soil Extraction from Sentinel-2 Images with Weakly Supervised and Spectral Index 燚. 赵 https://doi.org/10.12677/JSTA.2024.121005
116. High-Resolution Land-Cover Mapping Based on a Cross-Resolution Deep Learning Framework and Available Low-Resolution Labels X. Qi et al. https://doi.org/10.1109/JSTARS.2023.3342994
117. A Workflow for Collecting and Preprocessing Sentinel-1 Images for Time Series Prediction Suitable for Deep Learning Algorithms W. Moskolaï et al. https://doi.org/10.3390/geomatics2040024
118. Large-Scale Land Cover Mapping Framework Based on Prior Product Label Generation: A Case Study of Cambodia H. Zhu et al. https://doi.org/10.3390/rs16132443
119. Meta-FSEO: A Meta-Learning Fast Adaptation with Self-Supervised Embedding Optimization for Few-Shot Remote Sensing Scene Classification Y. Li et al. https://doi.org/10.3390/rs13142776
120. Two-Way Generation of High-Resolution EO and SAR Images via Dual Distortion-Adaptive GANs Y. Qing et al. https://doi.org/10.3390/rs15071878
121. A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks C. Qiu et al. https://doi.org/10.1016/j.isprsjprs.2020.01.028
122. Cloud-Aware Dual-Path Prompt Learning with CLIP for Few-Shot Fine-Grained Ship Classification in Mixed-Sky Remote Sensing Imagery Y. Song et al. https://doi.org/10.3390/rs18111815
123. Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications T. Hoeser et al. https://doi.org/10.3390/rs12183053
124. WTS: A Weakly towards Strongly Supervised Learning Framework for Remote Sensing Land Cover Classification Using Segmentation Models W. Zhang et al. https://doi.org/10.3390/rs13030394
125. Feature Interaction Learning Network for Cross-Spectral Image Patch Matching C. Yu et al. https://doi.org/10.1109/TIP.2023.3313488
126. Combining Deep Learning and Multi-Source GIS Methods to Analyze Urban and Greening Changes M. Francini et al. https://doi.org/10.3390/s23083805
127. Cloud-Guided Fusion With SAR-to-Optical Translation for Thick Cloud Removal X. Xiang et al. https://doi.org/10.1109/TGRS.2024.3431556
128. MetaMorphNet: Deep Shearlet and Flux-Based Fusion for Change Detection in Multi-Source Satellite Imagery S. Kasetty & K. Rajakumar https://doi.org/10.1109/ACCESS.2026.3661275
129. GLUENet: An Efficient Network for Remote Sensing Image Dehazing with Gated Linear Units and Efficient Channel Attention J. Fang et al. https://doi.org/10.3390/rs16081450
130. Learning-Based SAR–Optical Registration for Navigation: Insights From a Multiyear, Multiseason Continental-Scale Dataset S. Bertrand et al. https://doi.org/10.1109/TGRS.2025.3624474
131. MapInWild: A remote sensing dataset to address the question of what makes nature wild [Software and Data Sets] B. Ekim et al. https://doi.org/10.1109/MGRS.2022.3226525
132. Water body classification from high-resolution optical remote sensing imagery: Achievements and perspectives Y. Li et al. https://doi.org/10.1016/j.isprsjprs.2022.03.013
133. Flood Detection with SAR: A Review of Techniques and Datasets D. Amitrano et al. https://doi.org/10.3390/rs16040656
134. DeepForest: Novel Deep Learning Models for Land Use and Land Cover Classification Using Multi-Temporal and -Modal Sentinel Data of the Amazon Basin E. Cherif et al. https://doi.org/10.3390/rs14195000
135. TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing S. Ahlswede et al. https://doi.org/10.5194/essd-15-681-2023
136. Joint Network Combining Dual-Attention Fusion Modality and Two Specific Modalities for Land Cover Classification Using Optical and SAR Images X. Liu et al. https://doi.org/10.1109/JSTARS.2023.3347571
137. ISSLIDE: A New InSAR Dataset for Slow SLIding Area DEtection With Machine Learning A. Bralet et al. https://doi.org/10.1109/LGRS.2024.3365299
138. A Novel Coarse-to-Fine Deep Learning Registration Framework for Multimodal Remote Sensing Images D. Quan et al. https://doi.org/10.1109/TGRS.2023.3306042
139. cuFSDAF: An Enhanced Flexible Spatiotemporal Data Fusion Algorithm Parallelized Using Graphics Processing Units H. Gao et al. https://doi.org/10.1109/TGRS.2021.3080384
140. GCBF: Grouped Cross-Band Fusion Network for Multispectral Scene Classification J. Li et al. https://doi.org/10.1109/TGRS.2025.3572360
141. Explainable Earth Surface Forecasting Under Extreme Events O. Pellicer‐Valero et al. https://doi.org/10.1029/2024EF005446
142. Rearranging Pixels is a Powerful Black-Box Attack for RGB and Infrared Deep Learning Models J. Pomponi et al. https://doi.org/10.1109/ACCESS.2023.3241360
143. Change Captioning Meets SAR Imagery: A Multimodal Dataset and Bitemporal Modeling Framework E. Ozelbas et al. https://doi.org/10.1109/LGRS.2026.3687910
144. Cloud removal for optical remote sensing imagery using the SPA-CycleGAN network R. Jing et al. https://doi.org/10.1117/1.JRS.16.034520
145. A Unified Deep Learning Network for Remote Sensing Image Registration and Change Detection R. Zhou et al. https://doi.org/10.1109/TGRS.2023.3344751
146. HFTM: A Hierarchical Feature Transformer Framework With Multiscale Consensus for SAR–Optical Image Registration X. Zhang et al. https://doi.org/10.1109/LGRS.2026.3663870
147. Cloud Removal Based on SAR-Optical Remote Sensing Data Fusion via a Two-Flow Network R. Mao et al. https://doi.org/10.1109/JSTARS.2022.3203508
148. Multimodal Diffusion Bridge With Attention-Based SAR Fusion for Satellite Image Cloud Removal Y. Hu et al. https://doi.org/10.1109/TGRS.2025.3604654
149. AgriSen-COG, a Multicountry, Multitemporal Large-Scale Sentinel-2 Benchmark Dataset for Crop Mapping Using Deep Learning T. Selea https://doi.org/10.3390/rs15122980
150. Exploring Archetypes of Tropical Fire-Related Forest Disturbances Based on Dense Optical and Radar Satellite Data and Active Fire Alerts J. Balling et al. https://doi.org/10.3390/f12040456
151. Hash-Based Remote Sensing Image Retrieval L. Han et al. https://doi.org/10.1109/TGRS.2024.3429350
152. Mapping Dwellings in IDP/Refugee Settlements Using Deep Learning O. Ghorbanzadeh et al. https://doi.org/10.3390/rs14246382
153. Domain Adaptation for Mapping LCZs in Sub-Saharan Africa With Remote Sensing: A Comprehensive Approach to Health Data Analysis B. Rousse et al. https://doi.org/10.1109/JSTARS.2024.3421284
154. Super-Resolution of Remote Sensing Images for ×4 Resolution without Reference Images Y. Li et al. https://doi.org/10.3390/electronics11213474
155. High-Resolution Land Use Land Cover Dataset for Meteorological Modelling—Part 2: ECOCLIMAP-SG-ML an Ensemble Land Cover Map T. Rieutord et al. https://doi.org/10.3390/land13111875
156. Frequency-conditioned spatially adaptive decoding for bitemporal remote sensing segmentation S. Kasetty & R. Krishnan https://doi.org/10.1038/s41598-026-46611-w
157. SSGAN: Cloud removal in satellite images using spatiospectral generative adversarial network S. Ghildiyal et al. https://doi.org/10.1016/j.eja.2024.127333
158. Spatial and Spectral Translation of Landsat 8 to Sentinel-2 Using Conditional Generative Adversarial Networks R. Mukherjee & D. Liu https://doi.org/10.3390/rs15235502
159. CDPrompt: Multimodal Change Detection With In-Domain Prompt in Missing Modality Scenarios L. Zhang et al. https://doi.org/10.1109/TGRS.2025.3598803
160. SOSSF: Landsat-8 Image Synthesis on the Blending of Sentinel-1 and MODIS Data Y. Xia et al. https://doi.org/10.1109/TGRS.2024.3352662
161. Hybrid cGAN: Coupling Global and Local Features for SAR-to-Optical Image Translation Z. Wang et al. https://doi.org/10.1109/TGRS.2022.3212208
162. Structure similarity virtual map generation network for optical and SAR image matching S. Chen & L. Mei https://doi.org/10.3389/fphy.2024.1287050
163. AN OVERVIEW OF MULTIMODAL LEARNING: CONCEPTS, CHALLENGES, APPLICATIONS AND DATASETS A. Ateş & U. Yurtsever https://doi.org/10.36306/konjes.1716813
164. SCT-CR: A synergistic convolution-transformer modeling method using SAR-optical data fusion for cloud removal J. Ma et al. https://doi.org/10.1016/j.jag.2024.103909
165. AMM-FuseNet: Attention-Based Multi-Modal Image Fusion Network for Land Cover Mapping W. Ma et al. https://doi.org/10.3390/rs14184458
166. A Combined Convolutional Neural Network for Urban Land-Use Classification with GIS Data J. Yu et al. https://doi.org/10.3390/rs14051128
167. Seeing Through Clouds in Satellite Images M. Zhao et al. https://doi.org/10.1109/TGRS.2023.3239592
168. Tracking U.S. Land Cover Changes: A Dataset of Sentinel-2 Imagery and Dynamic World Labels (2016–2024) A. Rangel et al. https://doi.org/10.3390/data10050067
169. Sen-2 LULC: Land use land cover dataset for deep learning approaches S. Sawant et al. https://doi.org/10.1016/j.dib.2023.109724
170. A Time–Frequency-Aware Hierarchical Feature Optimization Method for SAR Jamming Recognition Z. Zhang et al. https://doi.org/10.1109/TAES.2025.3563141
171. Reproducibility and Replicability in SAR Remote Sensing T. Balz & F. Rocca https://doi.org/10.1109/JSTARS.2020.3005912
172. Time-series surface water reconstruction method (TSWR) based on spatial distance relationship of multi-stage water boundaries M. Li et al. https://doi.org/10.1080/17538947.2022.2159553
173. SpaceNet 9—Cross-Sensor Alignment of Optical and SAR Imagery R. Hänsch et al. https://doi.org/10.1109/JSTARS.2026.3666103
174. Machine and deep learning for sentinel-based land use and land cover change detection: a systematic review and future outlook A. Nigar et al. https://doi.org/10.1007/s10661-026-15558-w
175. Improving 3-m Resolution Land Cover Mapping through Efficient Learning from an Imperfect 10-m Resolution Map R. Dong et al. https://doi.org/10.3390/rs12091418
176. Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives X. Zhu et al. https://doi.org/10.1109/MGRS.2020.3046356
177. Deep Learning Methods for Semantic Segmentation in Remote Sensing with Small Data: A Survey A. Yu et al. https://doi.org/10.3390/rs15204987
178. MM-CGAN: a multi-module conditioned generative adversarial network for sar image to optical image translation F. Dong & C. Wang https://doi.org/10.1080/2150704X.2025.2564906
179. Uncertainty-Based Human-in-the-Loop Deep Learning for Land Cover Segmentation C. García Rodríguez et al. https://doi.org/10.3390/rs12223836
180. Vegetation Mapping in Heterogeneous Forest–Shrub–Grass Ecosystems Using Fused High-Resolution Optical and SAR Data Q. Pang et al. https://doi.org/10.3390/rs18091373
181. Advances and Challenges in Multimodal Remote Sensing Image Registration B. Zhu et al. https://doi.org/10.1109/JMASS.2023.3244848
182. Advancing agricultural remote sensing: A comprehensive review of deep supervised and Self-Supervised Learning for crop monitoring M. da Silva et al. https://doi.org/10.1016/j.cag.2025.104434
183. Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works C. Tao et al. https://doi.org/10.1109/TGRS.2023.3276853
184. Land use land cover classification using Sentinel imagery based on deep learning models S. Sawant & J. Ghosh https://doi.org/10.1007/s12040-024-02298-z
185. WH-MAVS: A Novel Dataset and Deep Learning Benchmark for Multiple Land Use and Land Cover Applications J. Yuan et al. https://doi.org/10.1109/JSTARS.2022.3142898
186. Land Cover Classification in the Antioquia Region of the Tropical Andes Using NICFI Satellite Data Program Imagery and Semantic Segmentation Techniques L. Gomez-Ossa et al. https://doi.org/10.3390/data8120185
187. SARCLIP: a multimodal foundation framework for SAR imagery via contrastive language-image pre-training C. Jiang et al. https://doi.org/10.1016/j.isprsjprs.2025.10.017
188. Self-Supervised Classification of SAR Images With Optical Image Assistance C. Li et al. https://doi.org/10.1109/TGRS.2023.3330826
189. Better, Not Just More: Data-centric machine learning for Earth observation R. Roscher et al. https://doi.org/10.1109/MGRS.2024.3470986
190. Wavelet-Based Deep Generative Framework for Super Resolution of Low-Resolution Labeled Maps and Weak-Supervised Learning A. Singh & L. Bruzzone https://doi.org/10.1109/TGRS.2025.3574396
191. Efficient and Robust: A Cross-Modal Registration Deep Wavelet Learning Method for Remote Sensing Images D. Quan et al. https://doi.org/10.1109/JSTARS.2023.3276409
192. Meta-learning to address diverse Earth observation problems across resolutions M. Rußwurm et al. https://doi.org/10.1038/s43247-023-01146-0
193. Vision-Language Models in Remote Sensing: Current progress and future trends X. Li et al. https://doi.org/10.1109/MGRS.2024.3383473
194. Generalization Enhancement Strategies to Enable Cross-Year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples S. Khallaghi et al. https://doi.org/10.3390/rs17030474
195. Harnessing Earth Observation and Machine Learning for Sustainable Development Goals T. Abbas et al. https://doi.org/10.1002/widm.70051
196. A Mamba-Based Approach for Super-Resolution and Speckle Noise Reconstruction of SAR Images in Remote Sensing Applications S. Luan et al. https://doi.org/10.1109/JSTARS.2025.3641890
197. Advancing Earth observation: a survey on AI-powered image processing in satellites A. Duggan et al. https://doi.org/10.1080/22797254.2025.2567921
198. Cosine Similarity Template Matching Networks for Optical and SAR Image Registration W. Xiong et al. https://doi.org/10.1109/JSTARS.2024.3504555
199. OS-MambaDiff: An Optical-SAR Fusion Diffusion Framework With Mamba for Robust Cloud Removal in Remote Sensing Imagery P. Yang et al. https://doi.org/10.1109/JSTARS.2026.3673264
200. CBEN—A Multimodal Machine Learning Dataset for Cloud-Robust Remote Sensing Image Understanding M. Stricker et al. https://doi.org/10.3390/electronics15132927
201. Synthesis and evaluation of seamless, large-scale, multispectral satellite images using Generative Adversarial Networks on land use and land cover and Sentinel-2 data T. Dedring & A. Rienow https://doi.org/10.1080/15481603.2024.2364460
202. Multispectral Semantic Segmentation for Land Cover Classification: An Overview L. Thomas Ramos & A. Sappa https://doi.org/10.1109/JSTARS.2024.3438620
203. Detecting Canopy Gaps in Uneven-Aged Mixed Forests through the Combined Use of Unmanned Aerial Vehicle Imagery and Deep Learning N. Htun et al. https://doi.org/10.3390/drones8090484
204. 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
205. SAR-to-Optical Image Translation and Cloud Removal Based on Conditional Generative Adversarial Networks: Literature Survey, Taxonomy, Evaluation Indicators, Limits and Future Directions Q. Xiong et al. https://doi.org/10.3390/rs15041137
206. Interpretable Deep Learning Framework for Land Use and Land Cover Classification in Remote Sensing Using SHAP A. Temenos et al. https://doi.org/10.1109/LGRS.2023.3251652
207. Inter-satellite variability of grassland curing maps produced by different satellite sensors – Victoria, Australia S. Li https://doi.org/10.1080/17538947.2021.1900938
208. Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation J. Zhang et al. https://doi.org/10.3390/s24041185
209. Remote Sensing Mapping of Build-Up Land with Noisy Label via Fault-Tolerant Learning G. Xu et al. https://doi.org/10.3390/rs14092263
210. Global perspectives on sand dune patterns: Scale-adaptable classification using Landsat imagery and deep learning strategies Z. Zheng et al. https://doi.org/10.1016/j.isprsjprs.2024.10.002
211. A Flexible Multi-Temporal and Multi-Modal Framework for Sentinel-1 and Sentinel-2 Analysis Ready Data P. Upadhyay et al. https://doi.org/10.3390/rs14051120
212. Multi-Label Remote Sensing Image Land Cover Classification Based on a Multi-Dimensional Attention Mechanism H. You et al. https://doi.org/10.3390/rs15204979
213. BASHVS: A Multispectral and SAR Image Fusion Method Based on Bidirectional Aggregation of Saliency in Human Visual System X. Gong et al. https://doi.org/10.1109/TGRS.2025.3589428
214. Amplitude SAR Imagery Splicing Localization E. Cannas et al. https://doi.org/10.1109/ACCESS.2022.3161836
215. SAR-DeCR: Latent Diffusion for SAR-Fused Thick Cloud Removal M. Wang et al. https://doi.org/10.3390/rs17132241
216. Robust SAR-assisted cloud removal via supervised align-guided fusion and bidirectional hybrid reconstruction A. Wang et al. https://doi.org/10.1080/17538947.2025.2472909
217. CerraData-4 MM: A Multimodal Benchmark Dataset on Cerrado for Land Use and Land Cover Classification M. Miranda et al. https://doi.org/10.1109/JSTARS.2025.3585805
218. Rethinking the Flow: A Canal-Based Machine Learning Approach to Urban Flood Detection Q. Euler & C. Gerlein-Safdi https://doi.org/10.1109/TGRS.2026.3671457
219. Self-Supervised Learning in Remote Sensing: A review Y. Wang et al. https://doi.org/10.1109/MGRS.2022.3198244