Articles | Volume 15, issue 1
https://doi.org/10.5194/essd-15-113-2023
https://doi.org/10.5194/essd-15-113-2023
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

Related authors

Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard
Viola Steidl, Jonathan Louis Bamber, and Xiao Xiang Zhu
The Cryosphere, 19, 645–661, https://doi.org/10.5194/tc-19-645-2025,https://doi.org/10.5194/tc-19-645-2025, 2025
Short summary
Training for Emergencies - How Germany is Preparing for Large-Scale Emergencies Using the EUROMED 2024 Civil Protection Exercise as an Example
Veronika Gstaiger, Nils Machinia, Nina Merkle, Dominik Rosenbaum, Ronald Nippold, Manuel Muehlhaus, Pablo d’Angelo, Corentin Henry, Xiangtian Yuan, Reza Bahmanyar, Franz Kurz, and Christa-Maria Krieg
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-3-2024, 163–168, https://doi.org/10.5194/isprs-annals-X-3-2024-163-2024,https://doi.org/10.5194/isprs-annals-X-3-2024-163-2024, 2024
Learning Building Floor Numbers from Crowdsourced Streetview Images
Yifan Tian, Yao Sun, and Xiao Xiang Zhu
Abstr. Int. Cartogr. Assoc., 7, 171, https://doi.org/10.5194/ica-abs-7-171-2024,https://doi.org/10.5194/ica-abs-7-171-2024, 2024
Calving front monitoring at a subseasonal resolution: a deep learning application for Greenland glaciers
Erik Loebel, Mirko Scheinert, Martin Horwath, Angelika Humbert, Julia Sohn, Konrad Heidler, Charlotte Liebezeit, and Xiao Xiang Zhu
The Cryosphere, 18, 3315–3332, https://doi.org/10.5194/tc-18-3315-2024,https://doi.org/10.5194/tc-18-3315-2024, 2024
Short summary
ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models
Zhenghang Yuan, Zhitong Xiong, Lichao Mou, and Xiao Xiang Zhu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-140,https://doi.org/10.5194/essd-2024-140, 2024
Revised manuscript accepted for ESSD
Short summary

Related subject area

Domain: ESSD – Land | Subject: Land Cover and Land Use
A Sentinel-2 machine learning dataset for tree species classification in Germany
Maximilian Freudenberg, Sebastian Schnell, and Paul Magdon
Earth Syst. Sci. Data, 17, 351–367, https://doi.org/10.5194/essd-17-351-2025,https://doi.org/10.5194/essd-17-351-2025, 2025
Short summary
High-resolution mapping of global winter-triticeae crops using a sample-free identification method
Yangyang Fu, Xiuzhi Chen, Chaoqing Song, Xiaojuan Huang, Jie Dong, Qiongyan Peng, and Wenping Yuan
Earth Syst. Sci. Data, 17, 95–115, https://doi.org/10.5194/essd-17-95-2025,https://doi.org/10.5194/essd-17-95-2025, 2025
Short summary
A flux tower site attribute dataset intended for land surface modeling
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 117–134, https://doi.org/10.5194/essd-17-117-2025,https://doi.org/10.5194/essd-17-117-2025, 2025
Short summary
Advances in LUCAS Copernicus 2022: enhancing Earth observations with comprehensive in situ data on EU land cover and use
Raphaël d'Andrimont, Momchil Yordanov, Fernando Sedano, Astrid Verhegghen, Peter Strobl, Savvas Zachariadis, Flavia Camilleri, Alessandra Palmieri, Beatrice Eiselt, Jose Miguel Rubio Iglesias, and Marijn van der Velde
Earth Syst. Sci. Data, 16, 5723–5735, https://doi.org/10.5194/essd-16-5723-2024,https://doi.org/10.5194/essd-16-5723-2024, 2024
Short summary
Global 30 m seamless data cube (2000–2022) of land surface reflectance generated from Landsat 5, 7, 8, and 9 and MODIS Terra constellations
Shuang Chen, Jie Wang, Qiang Liu, Xiangan Liang, Rui Liu, Peng Qin, Jincheng Yuan, Junbo Wei, Shuai Yuan, Huabing Huang, and Peng Gong
Earth Syst. Sci. Data, 16, 5449–5475, https://doi.org/10.5194/essd-16-5449-2024,https://doi.org/10.5194/essd-16-5449-2024, 2024
Short summary

Cited articles

Adrian, J., Sagan, V., and Maimaitijiang, M.: Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine, ISPRS J. Photogramm., 175, 215–235, 2021. a
Al-Najjar, H. A., Kalantar, B., Pradhan, B., Saeidi, V., Halin, A. A., Ueda, N., and Mansor, S.: Land cover classification from fused DSM and UAV images using convolutional neural networks, Remote Sensing, 11, 1461, https://doi.org/10.3390/rs11121461, 2019. a
Brachmann, J., Baumgartner, A., and Gege, P.: The Calibration Home Base for Imaging Spectrometers, Journal of Large-Scale Research Facilities JLSRF, 2, https://doi.org/10.17815/jlsrf-2-137, 2016. a
d'Angelo, P. and Kurz, F.: Aircraft based real time bundle adjustment and digital surface model generation, in: ISPRS Geospatial Week 2019, 1643–1647, https://elib.dlr.de/127049/ (last access: 2 January 2023​​​​​​​), 2019. a
Du, B., Wei, Q., and Liu, R.: An improved quantum-behaved particle swarm optimization for endmember extraction, IEEE T. Geosci. Remote, 57, 6003–6017, 2019. a
Download
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.
Share
Altmetrics
Final-revised paper
Preprint