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

201 citations as recorded by crossref.

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67. SEN12MS-CR-TS: A Remote-Sensing Data Set for Multimodal Multitemporal Cloud Removal P. Ebel et al.
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82. OpenForest: a data catalog for machine learning in forest monitoring A. Ouaknine et al.
83. 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
84. MUMUCD: A Multimodal Multiclass Change Detection Dataset F. Serva et al.
85. On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID Y. Long et al.
86. Deep Learning Methods for SAR and Optical Image Fusion: A Review C. Guo et al.
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89. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing T. Kattenborn et al.
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94. 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.
95. Classification and Feature Extraction Algorithm for On-Orbit Spacecraft Based on Optical–SAR Multimodal Information B. Jie et al.
96. Contrastive Self-Supervised Learning for Globally Distributed Landslide Detection O. Ghorbanzadeh et al.
97. The Eyes of the Gods: A Survey of Unsupervised Domain Adaptation Methods Based on Remote Sensing Data M. Xu et al.
98. Deep Image Prior Amplitude SAR Image Anonymization E. Cannas et al.
99. Better, Not Just More: Data-centric machine learning for Earth observation R. Roscher et al.
100. NaSC-TG2: Natural Scene Classification With Tiangong-2 Remotely Sensed Imagery Z. Zhou et al.
101. 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.
102. Bare Soil Extraction from Sentinel-2 Images with Weakly Supervised and Spectral Index 燚. 赵
103. High-Resolution Land-Cover Mapping Based on a Cross-Resolution Deep Learning Framework and Available Low-Resolution Labels X. Qi et al.
104. A Workflow for Collecting and Preprocessing Sentinel-1 Images for Time Series Prediction Suitable for Deep Learning Algorithms W. Moskolaï et al.
105. Large-Scale Land Cover Mapping Framework Based on Prior Product Label Generation: A Case Study of Cambodia H. Zhu et al.
106. Meta-FSEO: A Meta-Learning Fast Adaptation with Self-Supervised Embedding Optimization for Few-Shot Remote Sensing Scene Classification Y. Li et al.
107. Two-Way Generation of High-Resolution EO and SAR Images via Dual Distortion-Adaptive GANs Y. Qing et al.
108. A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks C. Qiu et al.
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110. WTS: A Weakly towards Strongly Supervised Learning Framework for Remote Sensing Land Cover Classification Using Segmentation Models W. Zhang et al.
111. Feature Interaction Learning Network for Cross-Spectral Image Patch Matching C. Yu et al.
112. Combining Deep Learning and Multi-Source GIS Methods to Analyze Urban and Greening Changes M. Francini et al.
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116. Learning-Based SAR–Optical Registration for Navigation: Insights From a Multiyear, Multiseason Continental-Scale Dataset S. Bertrand et al.
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119. Flood Detection with SAR: A Review of Techniques and Datasets D. Amitrano et al.
120. 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.
121. TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing S. Ahlswede et al.
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123. ISSLIDE: A New InSAR Dataset for Slow SLIding Area DEtection With Machine Learning A. Bralet et al.
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128. Rearranging Pixels is a Powerful Black-Box Attack for RGB and Infrared Deep Learning Models J. Pomponi et al.
129. Change Captioning Meets SAR Imagery: A Multimodal Dataset and Bitemporal Modeling Framework E. Ozelbas et al.
130. Cloud removal for optical remote sensing imagery using the SPA-CycleGAN network R. Jing et al.
131. A Unified Deep Learning Network for Remote Sensing Image Registration and Change Detection R. Zhou et al.
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135. AgriSen-COG, a Multicountry, Multitemporal Large-Scale Sentinel-2 Benchmark Dataset for Crop Mapping Using Deep Learning T. Selea
136. Exploring Archetypes of Tropical Fire-Related Forest Disturbances Based on Dense Optical and Radar Satellite Data and Active Fire Alerts J. Balling et al.
137. Hash-Based Remote Sensing Image Retrieval L. Han et al.
138. Mapping Dwellings in IDP/Refugee Settlements Using Deep Learning O. Ghorbanzadeh et al.
139. Domain Adaptation for Mapping LCZs in Sub-Saharan Africa With Remote Sensing: A Comprehensive Approach to Health Data Analysis B. Rousse et al.
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154. A Time–Frequency-Aware Hierarchical Feature Optimization Method for SAR Jamming Recognition Z. Zhang et al.
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186. Multiclass Land Cover Mapping from Historical Orthophotos Using Domain Adaptation and Spatio-Temporal Transfer Learning W. Van den Broeck et al.
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188. Interpretable Deep Learning Framework for Land Use and Land Cover Classification in Remote Sensing Using SHAP A. Temenos et al.
189. Inter-satellite variability of grassland curing maps produced by different satellite sensors – Victoria, Australia S. Li
190. Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation J. Zhang et al.
191. Remote Sensing Mapping of Build-Up Land with Noisy Label via Fault-Tolerant Learning G. Xu et al.
192. Global perspectives on sand dune patterns: Scale-adaptable classification using Landsat imagery and deep learning strategies Z. Zheng et al.
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194. Multi-Label Remote Sensing Image Land Cover Classification Based on a Multi-Dimensional Attention Mechanism H. You et al.
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200. Rethinking the Flow: A Canal-Based Machine Learning Approach to Urban Flood Detection Q. Euler & C. Gerlein-Safdi
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