Articles | Volume 15, issue 1
https://doi.org/10.5194/essd-15-113-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-113-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
09 Jan 2023
Data description paper |
| 09 Jan 2023
| 09 Jan 2023
MDAS: a new multimodal benchmark dataset for remote sensing
Jingliang Hu
Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany
Rong Liu
Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany
Danfeng Hong
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany
now at: Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, 100094 Beijing, China
Andrés Camero
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany
Helmholtz AI, 85764 Neuherberg, Germany
Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany
now at: Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, 100094 Beijing, China
Mathias Schneider
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany
Franz Kurz
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany
Karl Segl
German Research Center for Geosciences (GFZ), Helmholtz Center Potsdam, Telegrafenberg A17, 14473 Potsdam, Germany
Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany
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17 citations as recorded by crossref.
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- Deep-Learning-Based Semantic Segmentation of Remote Sensing Images: A Survey L. Huang et al. 10.1109/JSTARS.2023.3335891
- NGST-Net: A N-Gram based Swin Transformer Network for improving multispectral and hyperspectral image fusion Z. Feng et al. 10.1080/17538947.2024.2359574
- DECT: Diffusion-Enhanced CNN–Transformer for Multisource Remote Sensing Data Classification G. Zhang et al. 10.1109/JSTARS.2024.3479212
- S2CrossMamba: Spatial–Spectral Cross-Mamba for Multimodal Remote Sensing Image Classification G. Zhang et al. 10.1109/LGRS.2024.3488036
- IFF-Net: Irregular Feature Fusion Network for Multimodal Remote Sensing Image Classification H. Wang et al. 10.3390/app14125061
- A Review of Optical and SAR Image Deep Feature Fusion in Semantic Segmentation C. Liu et al. 10.1109/JSTARS.2024.3424831
- SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data Z. Li et al. 10.5194/essd-15-4749-2023
- An Open Benchmark Dataset for Forest Characterization from Sentinel-1 and -2 Time Series S. Hauser et al. 10.3390/rs16030488
- Flood sequence mapping with multimodal remote sensing under the influence of dense vegetation Z. Li & I. Demir 10.1080/01431161.2024.2305629
- Transformer-based contrastive prototypical clustering for multimodal remote sensing data Y. Cai et al. 10.1016/j.ins.2023.119655
- Extended Vision Transformer (ExViT) for Land Use and Land Cover Classification: A Multimodal Deep Learning Framework J. Yao et al. 10.1109/TGRS.2023.3284671
- Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks D. Hong et al. 10.1016/j.rse.2023.113856
- Two-Stage Evolutionary Algorithm Based on Subspace Specified Searching for Hyperspectral Endmember Extraction C. Lei et al. 10.1109/JSTARS.2023.3333955
- Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications F. Mena et al. 10.1109/JSTARS.2024.3361556
1 citations as recorded by crossref.
Latest update: 13 Dec 2024
Short summary
Multimodal data fusion is an intuitive strategy to break the limitation of individual data in Earth observation. Here, we present a multimodal data set, named MDAS, consisting of synthetic aperture radar (SAR), multispectral, hyperspectral, digital surface model (DSM), and geographic information system (GIS) data for the city of Augsburg, Germany, along with baseline models for resolution enhancement, spectral unmixing, and land cover classification, three typical remote sensing applications.
Multimodal data fusion is an intuitive strategy to break the limitation of individual data in...
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