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

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20. Combining Deep Learning and Multi-Source GIS Methods to Analyze Urban and Greening Changes M. Francini et al. 10.3390/s23083805
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23. A Deep Learning Approach to an Enhanced Building Footprint and Road Detection in High-Resolution Satellite Imagery C. Ayala et al. 10.3390/rs13163135
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25. 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
26. Water body classification from high-resolution optical remote sensing imagery: Achievements and perspectives Y. Li et al. 10.1016/j.isprsjprs.2022.03.013
27. 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
28. 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
29. Learning-Based Near-Infrared Band Simulation with Applications on Large-Scale Landcover Classification X. Yuan et al. 10.3390/s23094179
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31. cuFSDAF: An Enhanced Flexible Spatiotemporal Data Fusion Algorithm Parallelized Using Graphics Processing Units H. Gao et al. 10.1109/TGRS.2021.3080384
32. Rearranging Pixels is a Powerful Black-Box Attack for RGB and Infrared Deep Learning Models J. Pomponi et al. 10.1109/ACCESS.2023.3241360
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38. Multi-Relation Attention Network for Image Patch Matching D. Quan et al. 10.1109/TIP.2021.3101414
39. 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
40. 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
41. Mapping Dwellings in IDP/Refugee Settlements Using Deep Learning O. Ghorbanzadeh et al. 10.3390/rs14246382
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46. Explaining the Effects of Clouds on Remote Sensing Scene Classification J. Gawlikowski et al. 10.1109/JSTARS.2022.3221788
47. Self-Distillation Feature Learning Network for Optical and SAR Image Registration D. Quan et al. 10.1109/TGRS.2022.3173476
48. AMM-FuseNet: Attention-Based Multi-Modal Image Fusion Network for Land Cover Mapping W. Ma et al. 10.3390/rs14184458
49. A Combined Convolutional Neural Network for Urban Land-Use Classification with GIS Data J. Yu et al. 10.3390/rs14051128
50. Google Earth Engine: A Global Analysis and Future Trends A. Velastegui-Montoya et al. 10.3390/rs15143675
51. Interband Retrieval and Classification Using the Multilabeled Sentinel-2 BigEarthNet Archive U. Chaudhuri et al. 10.1109/JSTARS.2021.3112209
52. Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning A. Ciocarlan & A. Stoian 10.3390/rs13214255
53. Reproducibility and Replicability in SAR Remote Sensing T. Balz & F. Rocca 10.1109/JSTARS.2020.3005912
54. 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
55. Improving 3-m Resolution Land Cover Mapping through Efficient Learning from an Imperfect 10-m Resolution Map R. Dong et al. 10.3390/rs12091418
56. Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives X. Zhu et al. 10.1109/MGRS.2020.3046356
57. Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images Z. Chen et al. 10.1109/JSTARS.2021.3053603
58. Uncertainty-Based Human-in-the-Loop Deep Learning for Land Cover Segmentation C. García Rodríguez et al. 10.3390/rs12223836
59. SEN12MS-CR-TS: A Remote-Sensing Data Set for Multimodal Multitemporal Cloud Removal P. Ebel et al. 10.1109/TGRS.2022.3146246
60. Advances and Challenges in Multimodal Remote Sensing Image Registration B. Zhu et al. 10.1109/JMASS.2023.3244848
61. 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
62. Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works C. Tao et al. 10.1109/TGRS.2023.3276853
63. 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
64. Efficient and Robust: A Cross-Modal Registration Deep Wavelet Learning Method for Remote Sensing Images D. Quan et al. 10.1109/JSTARS.2023.3276409
65. 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
66. Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities L. Zhang & L. Zhang 10.1109/MGRS.2022.3145854
67. 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
68. 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
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70. Multiclass Land Cover Mapping from Historical Orthophotos Using Domain Adaptation and Spatio-Temporal Transfer Learning W. Van den Broeck et al. 10.3390/rs14235911
71. 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
72. 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
73. 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
74. Inter-satellite variability of grassland curing maps produced by different satellite sensors – Victoria, Australia S. Li 10.1080/17538947.2021.1900938
75. On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID Y. Long et al. 10.1109/JSTARS.2021.3070368
76. Remote Sensing Mapping of Build-Up Land with Noisy Label via Fault-Tolerant Learning G. Xu et al. 10.3390/rs14092263
77. Spatially transferable dwelling extraction from Multi-Sensor imagery in IDP/Refugee Settlements: A meta-Learning approach G. Gella et al. 10.1016/j.jag.2023.103210
78. A Flexible Multi-Temporal and Multi-Modal Framework for Sentinel-1 and Sentinel-2 Analysis Ready Data P. Upadhyay et al. 10.3390/rs14051120
79. Amplitude SAR Imagery Splicing Localization E. Cannas et al. 10.1109/ACCESS.2022.3161836
80. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing T. Kattenborn et al. 10.1016/j.isprsjprs.2020.12.010
81. deepNIR: Datasets for Generating Synthetic NIR Images and Improved Fruit Detection System Using Deep Learning Techniques I. Sa et al. 10.3390/s22134721
82. Self-Supervised Learning in Remote Sensing: A review Y. Wang et al. 10.1109/MGRS.2022.3198244