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

110 citations as recorded by crossref.

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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
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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
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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. Distilled Heterogeneous Feature Alignment Network for SAR Image Semantic Segmentation M. Gao et al. 10.1109/LGRS.2023.3293160
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36. 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
37. 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
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48. AgriSen-COG, a Multicountry, Multitemporal Large-Scale Sentinel-2 Benchmark Dataset for Crop Mapping Using Deep Learning T. Selea 10.3390/rs15122980
49. 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
50. Multi-Relation Attention Network for Image Patch Matching D. Quan et al. 10.1109/TIP.2021.3101414
51. 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
52. 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
53. Mapping Dwellings in IDP/Refugee Settlements Using Deep Learning O. Ghorbanzadeh et al. 10.3390/rs14246382
54. Super-Resolution of Remote Sensing Images for ×4 Resolution without Reference Images Y. Li et al. 10.3390/electronics11213474
55. 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
56. 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
57. Semi-Supervised Learning for Joint SAR and Multispectral Land Cover Classification A. Montanaro et al. 10.1109/LGRS.2022.3195259
58. Land Use and Land Cover Classification Meets Deep Learning: A Review S. Zhao et al. 10.3390/s23218966
59. 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
60. Explaining the Effects of Clouds on Remote Sensing Scene Classification J. Gawlikowski et al. 10.1109/JSTARS.2022.3221788
61. Spatial and Spectral Translation of Landsat 8 to Sentinel-2 Using Conditional Generative Adversarial Networks R. Mukherjee & D. Liu 10.3390/rs15235502
62. 3DMAE: Joint SAR and Optical Representation Learning With Vertical Masking L. Zhang et al. 10.1109/LGRS.2023.3316193
63. SOSSF: Landsat-8 Image Synthesis on the Blending of Sentinel-1 and MODIS Data Y. Xia et al. 10.1109/TGRS.2024.3352662
64. Hybrid cGAN: Coupling Global and Local Features for SAR-to-Optical Image Translation Z. Wang et al. 10.1109/TGRS.2022.3212208
65. Structure similarity virtual map generation network for optical and SAR image matching S. Chen & L. Mei 10.3389/fphy.2024.1287050
66. Self-Distillation Feature Learning Network for Optical and SAR Image Registration D. Quan et al. 10.1109/TGRS.2022.3173476
67. AMM-FuseNet: Attention-Based Multi-Modal Image Fusion Network for Land Cover Mapping W. Ma et al. 10.3390/rs14184458
68. A Combined Convolutional Neural Network for Urban Land-Use Classification with GIS Data J. Yu et al. 10.3390/rs14051128
69. Google Earth Engine: A Global Analysis and Future Trends A. Velastegui-Montoya et al. 10.3390/rs15143675
70. Interband Retrieval and Classification Using the Multilabeled Sentinel-2 BigEarthNet Archive U. Chaudhuri et al. 10.1109/JSTARS.2021.3112209
71. Seeing Through Clouds in Satellite Images M. Zhao et al. 10.1109/TGRS.2023.3239592
72. Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning A. Ciocarlan & A. Stoian 10.3390/rs13214255
73. Sen-2 LULC: Land use land cover dataset for deep learning approaches S. Sawant et al. 10.1016/j.dib.2023.109724
74. Reproducibility and Replicability in SAR Remote Sensing T. Balz & F. Rocca 10.1109/JSTARS.2020.3005912
75. 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
76. Improving 3-m Resolution Land Cover Mapping through Efficient Learning from an Imperfect 10-m Resolution Map R. Dong et al. 10.3390/rs12091418
77. Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives X. Zhu et al. 10.1109/MGRS.2020.3046356
78. Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images Z. Chen et al. 10.1109/JSTARS.2021.3053603
79. Deep Learning Methods for Semantic Segmentation in Remote Sensing with Small Data: A Survey A. Yu et al. 10.3390/rs15204987
80. Uncertainty-Based Human-in-the-Loop Deep Learning for Land Cover Segmentation C. García Rodríguez et al. 10.3390/rs12223836
81. SEN12MS-CR-TS: A Remote-Sensing Data Set for Multimodal Multitemporal Cloud Removal P. Ebel et al. 10.1109/TGRS.2022.3146246
82. Advances and Challenges in Multimodal Remote Sensing Image Registration B. Zhu et al. 10.1109/JMASS.2023.3244848
83. 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
84. Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works C. Tao et al. 10.1109/TGRS.2023.3276853
85. 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
86. 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
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88. Efficient and Robust: A Cross-Modal Registration Deep Wavelet Learning Method for Remote Sensing Images D. Quan et al. 10.1109/JSTARS.2023.3276409
89. Beyond Supervised Learning in Remote Sensing: A Systematic Review of Deep Learning Approaches B. Hosseiny et al. 10.1109/JSTARS.2023.3316733
90. Meta-learning to address diverse Earth observation problems across resolutions M. Rußwurm et al. 10.1038/s43247-023-01146-0
91. Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring M. Tom et al. 10.1109/TGRS.2022.3211184
92. 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
93. Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities L. Zhang & L. Zhang 10.1109/MGRS.2022.3145854
94. 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
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96. 2020 IEEE GRSS Data Fusion Contest: Global Land Cover Mapping With Weak Supervision [Technical Committees] N. Yokoya et al. 10.1109/MGRS.2020.2970124
97. Multiclass Land Cover Mapping from Historical Orthophotos Using Domain Adaptation and Spatio-Temporal Transfer Learning W. Van den Broeck et al. 10.3390/rs14235911
98. 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
99. 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
100. 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
101. Inter-satellite variability of grassland curing maps produced by different satellite sensors – Victoria, Australia S. Li 10.1080/17538947.2021.1900938
102. Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation J. Zhang et al. 10.3390/s24041185
103. On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID Y. Long et al. 10.1109/JSTARS.2021.3070368
104. Remote Sensing Mapping of Build-Up Land with Noisy Label via Fault-Tolerant Learning G. Xu et al. 10.3390/rs14092263
105. A Flexible Multi-Temporal and Multi-Modal Framework for Sentinel-1 and Sentinel-2 Analysis Ready Data P. Upadhyay et al. 10.3390/rs14051120
106. Multi-Label Remote Sensing Image Land Cover Classification Based on a Multi-Dimensional Attention Mechanism H. You et al. 10.3390/rs15204979
107. Amplitude SAR Imagery Splicing Localization E. Cannas et al. 10.1109/ACCESS.2022.3161836
108. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing T. Kattenborn et al. 10.1016/j.isprsjprs.2020.12.010
109. deepNIR: Datasets for Generating Synthetic NIR Images and Improved Fruit Detection System Using Deep Learning Techniques I. Sa et al. 10.3390/s22134721
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