Loading [MathJax]/jax/output/HTML-CSS/fonts/TeX/fontdata.js
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

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, 17, 1245–1263, https://doi.org/10.5194/essd-17-1245-2025,https://doi.org/10.5194/essd-17-1245-2025, 2025
Short summary
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

Related subject area

Domain: ESSD – Land | Subject: Land Cover and Land Use
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, 17, 1245–1263, https://doi.org/10.5194/essd-17-1245-2025,https://doi.org/10.5194/essd-17-1245-2025, 2025
Short summary
Aboveground biomass dataset from SMOS L-band vegetation optical depth and reference maps
Simon Boitard, Arnaud Mialon, Stéphane Mermoz, Nemesio J. Rodríguez-Fernández, Philippe Richaume, Julio César Salazar-Neira, Stéphane Tarot, and Yann H. Kerr
Earth Syst. Sci. Data, 17, 1101–1119, https://doi.org/10.5194/essd-17-1101-2025,https://doi.org/10.5194/essd-17-1101-2025, 2025
Short summary
GMIE: a global maximum irrigation extent and central pivot irrigation system dataset derived via irrigation performance during drought stress and deep learning methods
Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, Yuming Lu, and Yifan Li
Earth Syst. Sci. Data, 17, 855–880, https://doi.org/10.5194/essd-17-855-2025,https://doi.org/10.5194/essd-17-855-2025, 2025
Short summary
Annual vegetation maps in the Qinghai–Tibet Plateau (QTP) from 2000 to 2022 based on MODIS series satellite imagery
Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, and Mengzi Zhou
Earth Syst. Sci. Data, 17, 773–797, https://doi.org/10.5194/essd-17-773-2025,https://doi.org/10.5194/essd-17-773-2025, 2025
Short summary
Time series of Landsat-based bimonthly and annual spectral indices for continental Europe for 2000–2022
Xuemeng Tian, Davide Consoli, Martijn Witjes, Florian Schneider, Leandro Parente, Murat Şahin, Yu-Feng Ho, Robert Minařík, and Tomislav Hengl
Earth Syst. Sci. Data, 17, 741–772, https://doi.org/10.5194/essd-17-741-2025,https://doi.org/10.5194/essd-17-741-2025, 2025
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...
Share
Altmetrics
Final-revised paper
Preprint