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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume V-2-2022
https://doi.org/10.5194/isprs-annals-V-2-2022-283-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-annals-V-2-2022-283-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
17 May 2022
| 17 May 2022
ROAD CONDITION ASSESSMENT FROM AERIAL IMAGERY USING DEEP LEARNING
N. Merkle
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
C. Henry
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
S. M. Azimi
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
F. Kurz
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
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Short summary
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.
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