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ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
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Wald5Dplus

Authors: Hauser, Sarah; Schmitt, Andreas; Krzystek, Peter; Ruhhammer, Michael;
Abstract

Wald5Dplus The "Wald5Dplus" project, funded by the space management of the German Aerospace Center e.V. with funds from the Federal Ministry for Economic Affairs and Energy, creates a syntactic training data set from multitemporal Sentinel-1 and Sentinel-2 satellite images and assigns the individual elements semantic labels that derived from flights. The satellite missions Sentinel-1 (C-Band Synthetic Aperture Radar) and Sentinel-2 (Multispectral Imager) are part of the Copernicus program of the European Commission and the European Space Agency. They provide weekly recordings in a 10 m grid of the whole of Europe (unfortunately depending on the weather in the case of Sentinel-2), which are freely available to every potential user. In a year, around sixty images are collected per satellite. As part of this project, the grid values are calculated for three selected forest areas in the north-south direction (first dimension), in the east-west direction (second dimension), polarimetrically by Sentinel-1 (third dimension), spectrally by Sentinel-2 (fourth dimension ) over time (fifth dimension) in an Analysis Ready Data Cube and provided with semantic labels (“plus”). The labels come from flights of the test areas with aircraft or UAV-borne laser scanners and multispectral cameras and therefore have a very high spatial resolution. They are intended to provide information about certain forest parameters such as tree species, number of trees, crown area, crown height and initial crown height. The point clouds are evaluated using specially developed, patented algorithms and result in the required semantic labels, which are then aggregated onto the spatial grid of the satellite data. Using the data cube, machine learning methods are pre-trained in order to be used in other projects. The entire package will be made available to all interested scientists worldwide free of charge as a benchmark data set via the ML4Earth platform.

Keywords

Machine learning, Forestry, Remote sensing

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average