MDAS: a new multimodal benchmark dataset for remote sensing

Hu, Jingliang; Liu, Rong; Hong, Danfeng; Camero, Andrés; Yao, Jing; Schneider, Mathias; Kurz, Franz; Segl, Karl; Zhu, Xiao Xiang

doi:https://doi.org/10.5194/essd-15-113-2023

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.

https://doi.org/10.5194/essd-15-113-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 1

Data description paper

|

09 Jan 2023

Data description paper |

| 09 Jan 2023

MDAS: a new multimodal benchmark dataset for remote sensing

Jingliang Hu, Rong Liu, Danfeng Hong, Andrés Camero, Jing Yao, Mathias Schneider, Franz Kurz, Karl Segl, and Xiao Xiang Zhu

Data sets

MDAS: A New Multimodal Benchmark Dataset for Remote Sensing Jingliang Hu, Rong Liu, Danfeng Hong, Andrés Camero, Jing Yao, Mathias Schneider, Franz Kurz, Karl Segl, and Xiao Xiang Zhu https://doi.org/10.14459/2022mp1657312

Model code and software

zhu-xlab/augsburg_Multimodal_Data_Set_MDaS: Accepted data set paper (v1.0.0) Jingliang Hu, Rong Liu, Danfeng Hong, and Andrés Camero https://doi.org/10.5281/zenodo.7428215

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.