license: cc-by-4.0
This dataset deals with the mapping of forest species using multi-modal Earth Observation data.
It is an extension of the existing dataset TreeSatAI by Ahlswede et al.
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.
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 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. 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. 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.
The Sentinel Time Series are provided for each patch in HDF format (.h5) with several datasets :
sen-1-asc-data: Sentinel-1 ascending orbit backscattering coefficient data (Tx2x6x6)sen-1-asc-products: Sentinel-1 ascending orbit product names (T)sen-1-des-data: Sentinel-1 descending orbit backscattering coefficient data (Tx2x6x6)sen-1-des-data: Sentinel-1 ascending orbit product names (T)sen-2-data: Sentinel-2 Level-2 BOA reflectances (Tx10x6x6)sen-2-masks: Sentinel-2 cloud cover masks (Tx2x6x6)sen-2-products: Sentinel-2 product names (T)
To access the data in python you can use :
import h5py
with h5py.File(path/to/h5/file, 'r') as h5:
sen_1_asc_data = f['sen-1-asc-data'][:]
sen_1_asc_products = f['sen-1-asc-products'][:]
sen_1_des_data = f['sen-1-des-data'][:]
sen_1_des_products = f['sen-1-des-products'][:]
sen_2_data = f['sen-2-data'][:]
sen_2_products = f['sen-2-products'][:]
sen_2_masks = f['sen-2-masks'][:]