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
Xiao Xiang Zhu
CORRESPONDING AUTHOR
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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11 citations as recorded by crossref.
- 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
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- Flood sequence mapping with multimodal remote sensing under the influence of dense vegetation Z. Li & I. Demir 10.1080/01431161.2024.2305629
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- 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: 19 Apr 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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