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 article
| 
09 Jan 2023
Data description article |
| 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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51 citations as recorded by crossref.
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- Review of methane emission source tracing methods in oilfield regions Y. Liu et al.
- Dual-Branch Rectangle Transformer for Hierarchical Hyperspectral Super Resolution via Spectral Reversion Contrastive Learning K. Xu et al.
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- Contrastive Prototype Clustering for Multimodal Remote Sensing Data Based on Spectral–Spatial Cross Mamba L. Lv et al.
- Multilevel Spatial Spectral Difference and Geometric Constraint Model for Airborne Snapshot Spectral Imaging M. Hao et al.
- Deep Learning in Remote Sensing Image Fusion: Methods, protocols, data, and future perspectives G. Vivone et al.
- Low-frequency constrained generative adversarial network: An attack framework for remote sensing image scene classification H. Meng et al.
- Two-Stage Evolutionary Algorithm Based on Subspace Specified Searching for Hyperspectral Endmember Extraction C. Lei et al.
- Feature Reconstruction Guided Fusion Network for Hyperspectral and LiDAR Classification Z. Li et al.
- MCAMamba: Multilevel Cross-Modal Attention-Guided State-Space Model for Multisource Remote Sensing Image Classification M. Dou et al.
- Bilinear Parallel Fourier Transformer for Multimodal Remote Sensing Classification Y. Chen et al.
- Off-Road Trafficability Assessment With Remote Sensing Imagery and Incomplete Auxiliary Data via a Cross-Modal Channel Feature Fusion Network K. He et al.
- A Dual-Modal Framework Integrating SAR-Based Change Screening and Optical-Scene-Informed Identification for High-Frequency Monitoring of Construction-Ready Bare Land W. Song et al.
- FLAIR-HUB: Large-scale multimodal dataset for land cover and crop mapping A. Garioud et al.
- A Review of Downscaling Multi-Source Satellite Data Fusion Methods B. Kumar et al.
- NGST-Net: A N-Gram based Swin Transformer Network for improving multispectral and hyperspectral image fusion Z. Feng et al.
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- Anchor-guided multi-view fuzzy clustering for hyperspectral and LiDAR images L. Xiao et al.
- IFF-Net: Irregular Feature Fusion Network for Multimodal Remote Sensing Image Classification H. Wang et al.
- On the foundations of Earth foundation models X. Zhu et al.
- 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.
- An Open Benchmark Dataset for Forest Characterization from Sentinel-1 and -2 Time Series S. Hauser et al.
- Representative Spectral Correlation Network for Multisource Remote Sensing Image Classification C. Gong et al.
- DSF2-NAS: Dual-Stage Feature Fusion via Network Architecture Search for Classification of Multimodal Remote Sensing Images S. Feng et al.
- Efficient Spatial-Channel Spiking Neural Network for Multimodal Remote Sensing Data Classification X. He et al.
- Extended Vision Transformer (ExViT) for Land Use and Land Cover Classification: A Multimodal Deep Learning Framework J. Yao et al.
- Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks D. Hong et al.
- Calibration-Enhanced Multi-Awareness Network for Joint Classification of Hyperspectral and LiDAR Data Q. Zhang et al.
- Collaborative Classification of Hyperspectral and LiDAR Data Based on Dynamic Multiple Fractional Fourier Domains Fusion B. Qin et al.
- A systematic literature review on deep learning approaches for small object detection J. Sayyad & K. Attarde
- MultiRS flood mapper: a google earth engine application for water extent mapping with multimodal remote sensing and quantile-based postprocessing Z. Li & I. Demir
- LKA-GFNet: Language Knowledge-Augmented Graph Fusion for Tri-Source Heterogeneous Remote Sensing Data Classification W. Cheng et al.
- Deep-Learning-Based Semantic Segmentation of Remote Sensing Images: A Survey L. Huang et al.
- A review of remote sensing in coastal aquaculture: data, geographic hotspots, methods, and challenges Y. Xu et al.
- S2CrossMamba: Spatial–Spectral Cross-Mamba for Multimodal Remote Sensing Image Classification G. Zhang et al.
- MMCTNet: multimodal cross-scale transformer network for hyperspectral and LiDAR/SAR image classification S. Gong et al.
- A Review of Optical and SAR Image Deep Feature Fusion in Semantic Segmentation C. Liu et al.
- AutoMergeNet: AutoML-Based M-Source Satellite Data Fusion Evaluated With Atmospheric Case Studies J. Wąsala et al.
- Flood sequence mapping with multimodal remote sensing under the influence of dense vegetation Z. Li & I. Demir
- Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications F. Mena et al.
Saved (final revised paper)
Latest update: 06 May 2026
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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