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README.md
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@@ -6,7 +6,9 @@ This dataset deals with the mapping of forest species using multi-modal Earth Ob
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It is based on the existing dataset TreeSatAI by Ahlswede et al1.
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While the original dataset only provides access to one Sentinel-1 & -2 image for each patch, this new dataset gathers all the available Sentinel-1 & -2 data spanning the same year for each patch.
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Ahlswede et al. introduced the TreeSatAI Benchmark Archive, a new dataset for tree species classification in Central Europe based on multi-sensor data from aerial,
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Sentinel-1 and Sentinel-2 1. The dataset contains labels of 20 European tree species (i.e., 15 tree genera) derived from forest administration data of the federal state of Lower Saxony, Germany.
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The authors propose models and guidelines for the application of the latest machine learning techniques for the task of tree species classification with multi-label data.
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Finally, they provide various benchmark experiments showcasing the information which can be derived from the different sensors including artificial neural networks and tree-based machine learning methods.
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It is based on the existing dataset TreeSatAI by Ahlswede et al1.
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While the original dataset only provides access to one Sentinel-1 & -2 image for each patch, this new dataset gathers all the available Sentinel-1 & -2 data spanning the same year for each patch.
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Ahlswede et al. (https://essd.copernicus.org/articles/15/681/2023/) introduced the TreeSatAI Benchmark Archive, a new dataset for tree species classification in Central Europe based on multi-sensor data from aerial,
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Sentinel-1 and Sentinel-2 1. The dataset contains labels of 20 European tree species (i.e., 15 tree genera) derived from forest administration data of the federal state of Lower Saxony, Germany.
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The authors propose models and guidelines for the application of the latest machine learning techniques for the task of tree species classification with multi-label data.
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Finally, they provide various benchmark experiments showcasing the information which can be derived from the different sensors including artificial neural networks and tree-based machine learning methods.
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While the original dataset only provides access to one Sentinel-1 & -2 image for each patch, this time-series dataset collects all the available Sentinel-1 & -2 data spanning the same year for each patch.
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