  Hyperspectral Imaging, employed in satellites for space remote sensing, like
HYPSO-1, faces constraints due to few labeled data sets, affecting the training
of AI models demanding these ground-truth annotations. In this work, we
introduce The HYPSO-1 Sea-Land-Cloud-Labeled Dataset, an open dataset with 200
diverse hyperspectral images from the HYPSO-1 mission, available in both raw
and calibrated forms for scientific research in Earth observation. Moreover, 38
of these images from different countries include ground-truth labels at
pixel-level totaling about 25 million spectral signatures labeled for
sea/land/cloud categories. To demonstrate the potential of the dataset and its
labeled subset, we have additionally optimized a deep learning model (1D Fully
Convolutional Network), achieving superior performance to the current state of
the art. The complete dataset, ground-truth labels, deep learning model, and
software code are openly accessible for download at the website
https://ntnu-smallsat-lab.github.io/hypso1_sea_land_clouds_dataset/ .
