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README.md

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 This dataset was introduced in the [ECCV24 paper](https://arxiv.org/pdf/2404.08351) OmniSat.
 
-The dataset is utilized in the paper [MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data](https://huggingface.co/papers/2508.10894). The code for MAESTRO can be found at: https://github.com/ignf/maestro
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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, 
 Sentinel-1 and Sentinel-2. 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. 
 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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 Notably, we aligned the year of the Sentinel Time Series with that of the aerial patch if it was 2017 or later. 
 For preceding years, considering minimal changes in the forest and the need for sufficient temporal context, we specifically chose the year 2017.
 
+The dataset is utilized in the paper [MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data](https://huggingface.co/papers/2508.10894). The code for MAESTRO can be found at: https://github.com/ignf/maestro
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