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

112 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. 10.1109/JSTARS.2021.3073965
2. 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 10.4995/raet.2022.15710
3. SwiMDiff: Scene-Wide Matching Contrastive Learning With Diffusion Constraint for Remote Sensing Image J. Tian et al. 10.1109/TGRS.2024.3371481
4. Multi-Scale Feature Fusion Network with Symmetric Attention for Land Cover Classification Using SAR and Optical Images D. Xu et al. 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. 10.1109/MGRS.2020.3033515
6. Forest Disturbance Detection via Self-Supervised and Transfer Learning With Sentinel-1&2 Images R. Kuzu et al. 10.1109/JSTARS.2024.3361183
7. The Eyes of the Gods: A Survey of Unsupervised Domain Adaptation Methods Based on Remote Sensing Data M. Xu et al. 10.3390/rs14174380
8. Deep Image Prior Amplitude SAR Image Anonymization E. Cannas et al. 10.3390/rs15153750
9. SSL4EO-S12: A large-scale multimodal, multitemporal dataset for self-supervised learning in Earth observation [Software and Data Sets] Y. Wang et al. 10.1109/MGRS.2023.3281651
10. NaSC-TG2: Natural Scene Classification With Tiangong-2 Remotely Sensed Imagery Z. Zhou et al. 10.1109/JSTARS.2021.3063096
11. High-Resolution Land Cover Mapping Through Learning With Noise Correction R. Dong et al. 10.1109/TGRS.2021.3068280
12. 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. 10.3390/s22062416
13. Self-Supervised Learning for Invariant Representations From Multi-Spectral and SAR Images P. Jain et al. 10.1109/JSTARS.2022.3204888
14. 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. 10.1109/JSTARS.2020.2995711
15. Bare Soil Extraction from Sentinel-2 Images with Weakly Supervised and Spectral Index 燚. 赵 10.12677/JSTA.2024.121005
16. There Are No Data Like More Data: Datasets for deep learning in Earth observation M. Schmitt et al. 10.1109/MGRS.2023.3293459
17. Towards global flood mapping onboard low cost satellites with machine learning G. Mateo-Garcia et al. 10.1038/s41598-021-86650-z
18. High-Resolution Land-Cover Mapping Based on a Cross-Resolution Deep Learning Framework and Available Low-Resolution Labels X. Qi et al. 10.1109/JSTARS.2023.3342994
19. A Workflow for Collecting and Preprocessing Sentinel-1 Images for Time Series Prediction Suitable for Deep Learning Algorithms W. Moskolaï et al. 10.3390/geomatics2040024
20. 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. 10.1080/01431161.2021.2012295
21. Meta-FSEO: A Meta-Learning Fast Adaptation with Self-Supervised Embedding Optimization for Few-Shot Remote Sensing Scene Classification Y. Li et al. 10.3390/rs13142776
22. Two-Way Generation of High-Resolution EO and SAR Images via Dual Distortion-Adaptive GANs Y. Qing et al. 10.3390/rs15071878
23. A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks C. Qiu et al. 10.1016/j.isprsjprs.2020.01.028
24. Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications T. Hoeser et al. 10.3390/rs12183053
25. WTS: A Weakly towards Strongly Supervised Learning Framework for Remote Sensing Land Cover Classification Using Segmentation Models W. Zhang et al. 10.3390/rs13030394
26. Review of pixel-level remote sensing image fusion based on deep learning Z. Wang et al. 10.1016/j.inffus.2022.09.008
27. Feature Interaction Learning Network for Cross-Spectral Image Patch Matching C. Yu et al. 10.1109/TIP.2023.3313488
28. Combining Deep Learning and Multi-Source GIS Methods to Analyze Urban and Greening Changes M. Francini et al. 10.3390/s23083805
29. Benchmarking and scaling of deep learning models for land cover image classification I. Papoutsis et al. 10.1016/j.isprsjprs.2022.11.012
30. Multisensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery P. Ebel et al. 10.1109/TGRS.2020.3024744
31. A Deep Learning Approach to an Enhanced Building Footprint and Road Detection in High-Resolution Satellite Imagery C. Ayala et al. 10.3390/rs13163135
32. GLUENet: An Efficient Network for Remote Sensing Image Dehazing with Gated Linear Units and Efficient Channel Attention J. Fang et al. 10.3390/rs16081450
33. Distilled Heterogeneous Feature Alignment Network for SAR Image Semantic Segmentation M. Gao et al. 10.1109/LGRS.2023.3293160
34. MapInWild: A remote sensing dataset to address the question of what makes nature wild [Software and Data Sets] B. Ekim et al. 10.1109/MGRS.2022.3226525
35. Water body classification from high-resolution optical remote sensing imagery: Achievements and perspectives Y. Li et al. 10.1016/j.isprsjprs.2022.03.013
36. Flood Detection with SAR: A Review of Techniques and Datasets D. Amitrano et al. 10.3390/rs16040656
37. 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. 10.3390/rs14195000
38. TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing S. Ahlswede et al. 10.5194/essd-15-681-2023
39. Joint Network Combining Dual-Attention Fusion Modality and Two Specific Modalities for Land Cover Classification Using Optical and SAR Images X. Liu et al. 10.1109/JSTARS.2023.3347571
40. ISSLIDE: A New InSAR Dataset for Slow SLIding Area DEtection With Machine Learning A. Bralet et al. 10.1109/LGRS.2024.3365299
41. Learning-Based Near-Infrared Band Simulation with Applications on Large-Scale Landcover Classification X. Yuan et al. 10.3390/s23094179
42. A Novel Coarse-to-Fine Deep Learning Registration Framework for Multimodal Remote Sensing Images D. Quan et al. 10.1109/TGRS.2023.3306042
43. cuFSDAF: An Enhanced Flexible Spatiotemporal Data Fusion Algorithm Parallelized Using Graphics Processing Units H. Gao et al. 10.1109/TGRS.2021.3080384
44. Rearranging Pixels is a Powerful Black-Box Attack for RGB and Infrared Deep Learning Models J. Pomponi et al. 10.1109/ACCESS.2023.3241360
45. Cloud removal for optical remote sensing imagery using the SPA-CycleGAN network R. Jing et al. 10.1117/1.JRS.16.034520
46. A Unified Deep Learning Network for Remote Sensing Image Registration and Change Detection R. Zhou et al. 10.1109/TGRS.2023.3344751
47. Cloud Removal Based on SAR-Optical Remote Sensing Data Fusion via a Two-Flow Network R. Mao et al. 10.1109/JSTARS.2022.3203508
48. FG-GAN: A Fine-Grained Generative Adversarial Network for Unsupervised SAR-to-Optical Image Translation X. Yang et al. 10.1109/TGRS.2022.3165371
49. AgriSen-COG, a Multicountry, Multitemporal Large-Scale Sentinel-2 Benchmark Dataset for Crop Mapping Using Deep Learning T. Selea 10.3390/rs15122980
50. Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion A. Meraner et al. 10.1016/j.isprsjprs.2020.05.013
51. Multi-Relation Attention Network for Image Patch Matching D. Quan et al. 10.1109/TIP.2021.3101414
52. Exploring Archetypes of Tropical Fire-Related Forest Disturbances Based on Dense Optical and Radar Satellite Data and Active Fire Alerts J. Balling et al. 10.3390/f12040456
53. Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience: A Survey M. Savelonas et al. 10.3390/rs14236017
54. Mapping Dwellings in IDP/Refugee Settlements Using Deep Learning O. Ghorbanzadeh et al. 10.3390/rs14246382
55. Super-Resolution of Remote Sensing Images for ×4 Resolution without Reference Images Y. Li et al. 10.3390/electronics11213474
56. S1S2-Water: A Global Dataset for Semantic Segmentation of Water Bodies From Sentinel- 1 and Sentinel-2 Satellite Images M. Wieland et al. 10.1109/JSTARS.2023.3333969
57. A super-ensemble approach to map land cover types with high resolution over data-sparse African savanna landscapes L. Song et al. 10.1016/j.jag.2022.103152
58. Semi-Supervised Learning for Joint SAR and Multispectral Land Cover Classification A. Montanaro et al. 10.1109/LGRS.2022.3195259
59. Land Use and Land Cover Classification Meets Deep Learning: A Review S. Zhao et al. 10.3390/s23218966
60. MLRSNet: A multi-label high spatial resolution remote sensing dataset for semantic scene understanding X. Qi et al. 10.1016/j.isprsjprs.2020.09.020
61. Explaining the Effects of Clouds on Remote Sensing Scene Classification J. Gawlikowski et al. 10.1109/JSTARS.2022.3221788
62. Spatial and Spectral Translation of Landsat 8 to Sentinel-2 Using Conditional Generative Adversarial Networks R. Mukherjee & D. Liu 10.3390/rs15235502
63. 3DMAE: Joint SAR and Optical Representation Learning With Vertical Masking L. Zhang et al. 10.1109/LGRS.2023.3316193
64. SOSSF: Landsat-8 Image Synthesis on the Blending of Sentinel-1 and MODIS Data Y. Xia et al. 10.1109/TGRS.2024.3352662
65. Hybrid cGAN: Coupling Global and Local Features for SAR-to-Optical Image Translation Z. Wang et al. 10.1109/TGRS.2022.3212208
66. Structure similarity virtual map generation network for optical and SAR image matching S. Chen & L. Mei 10.3389/fphy.2024.1287050
67. Self-Distillation Feature Learning Network for Optical and SAR Image Registration D. Quan et al. 10.1109/TGRS.2022.3173476
68. AMM-FuseNet: Attention-Based Multi-Modal Image Fusion Network for Land Cover Mapping W. Ma et al. 10.3390/rs14184458
69. A Combined Convolutional Neural Network for Urban Land-Use Classification with GIS Data J. Yu et al. 10.3390/rs14051128
70. Google Earth Engine: A Global Analysis and Future Trends A. Velastegui-Montoya et al. 10.3390/rs15143675
71. Interband Retrieval and Classification Using the Multilabeled Sentinel-2 BigEarthNet Archive U. Chaudhuri et al. 10.1109/JSTARS.2021.3112209
72. Seeing Through Clouds in Satellite Images M. Zhao et al. 10.1109/TGRS.2023.3239592
73. Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning A. Ciocarlan & A. Stoian 10.3390/rs13214255
74. Sen-2 LULC: Land use land cover dataset for deep learning approaches S. Sawant et al. 10.1016/j.dib.2023.109724
75. Reproducibility and Replicability in SAR Remote Sensing T. Balz & F. Rocca 10.1109/JSTARS.2020.3005912
76. Time-series surface water reconstruction method (TSWR) based on spatial distance relationship of multi-stage water boundaries M. Li et al. 10.1080/17538947.2022.2159553
77. Improving 3-m Resolution Land Cover Mapping through Efficient Learning from an Imperfect 10-m Resolution Map R. Dong et al. 10.3390/rs12091418
78. Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives X. Zhu et al. 10.1109/MGRS.2020.3046356
79. Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images Z. Chen et al. 10.1109/JSTARS.2021.3053603
80. Deep Learning Methods for Semantic Segmentation in Remote Sensing with Small Data: A Survey A. Yu et al. 10.3390/rs15204987
81. Uncertainty-Based Human-in-the-Loop Deep Learning for Land Cover Segmentation C. García Rodríguez et al. 10.3390/rs12223836
82. SEN12MS-CR-TS: A Remote-Sensing Data Set for Multimodal Multitemporal Cloud Removal P. Ebel et al. 10.1109/TGRS.2022.3146246
83. Advances and Challenges in Multimodal Remote Sensing Image Registration B. Zhu et al. 10.1109/JMASS.2023.3244848
84. Deep Species Distribution Modeling From Sentinel-2 Image Time-Series: A Global Scale Analysis on the Orchid Family J. Estopinan et al. 10.3389/fpls.2022.839327
85. Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works C. Tao et al. 10.1109/TGRS.2023.3276853
86. WH-MAVS: A Novel Dataset and Deep Learning Benchmark for Multiple Land Use and Land Cover Applications J. Yuan et al. 10.1109/JSTARS.2022.3142898
87. 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. 10.3390/data8120185
88. Self-Supervised Classification of SAR Images With Optical Image Assistance C. Li et al. 10.1109/TGRS.2023.3330826
89. Efficient and Robust: A Cross-Modal Registration Deep Wavelet Learning Method for Remote Sensing Images D. Quan et al. 10.1109/JSTARS.2023.3276409
90. Beyond Supervised Learning in Remote Sensing: A Systematic Review of Deep Learning Approaches B. Hosseiny et al. 10.1109/JSTARS.2023.3316733
91. Meta-learning to address diverse Earth observation problems across resolutions M. Rußwurm et al. 10.1038/s43247-023-01146-0
92. Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring M. Tom et al. 10.1109/TGRS.2022.3211184
93. Global Land-Cover Mapping With Weak Supervision: Outcome of the 2020 IEEE GRSS Data Fusion Contest C. Robinson et al. 10.1109/JSTARS.2021.3063849
94. Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities L. Zhang & L. Zhang 10.1109/MGRS.2022.3145854
95. 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. 10.1080/01431161.2022.2054295
96. Cross-resolution national-scale land-cover mapping based on noisy label learning: A case study of China Y. Liu et al. 10.1016/j.jag.2023.103265
97. Multimodal Supervised Contrastive Learning in Remote Sensing Downstream Tasks P. Berg et al. 10.1109/LGRS.2024.3385995
98. 2020 IEEE GRSS Data Fusion Contest: Global Land Cover Mapping With Weak Supervision [Technical Committees] N. Yokoya et al. 10.1109/MGRS.2020.2970124
99. Multiclass Land Cover Mapping from Historical Orthophotos Using Domain Adaptation and Spatio-Temporal Transfer Learning W. Van den Broeck et al. 10.3390/rs14235911
100. 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. 10.3390/rs15041137
101. 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 10.1007/s41064-023-00238-y
102. Interpretable Deep Learning Framework for Land Use and Land Cover Classification in Remote Sensing Using SHAP A. Temenos et al. 10.1109/LGRS.2023.3251652
103. Inter-satellite variability of grassland curing maps produced by different satellite sensors – Victoria, Australia S. Li 10.1080/17538947.2021.1900938
104. Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation J. Zhang et al. 10.3390/s24041185
105. On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID Y. Long et al. 10.1109/JSTARS.2021.3070368
106. Remote Sensing Mapping of Build-Up Land with Noisy Label via Fault-Tolerant Learning G. Xu et al. 10.3390/rs14092263
107. A Flexible Multi-Temporal and Multi-Modal Framework for Sentinel-1 and Sentinel-2 Analysis Ready Data P. Upadhyay et al. 10.3390/rs14051120
108. Multi-Label Remote Sensing Image Land Cover Classification Based on a Multi-Dimensional Attention Mechanism H. You et al. 10.3390/rs15204979
109. Amplitude SAR Imagery Splicing Localization E. Cannas et al. 10.1109/ACCESS.2022.3161836
110. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing T. Kattenborn et al. 10.1016/j.isprsjprs.2020.12.010
111. deepNIR: Datasets for Generating Synthetic NIR Images and Improved Fruit Detection System Using Deep Learning Techniques I. Sa et al. 10.3390/s22134721
112. Self-Supervised Learning in Remote Sensing: A review Y. Wang et al. 10.1109/MGRS.2022.3198244