# An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Truth from the HYPSO-1 Satellite

arXiv: https://arxiv.org/abs/2308.13679 · HTML: https://mlsanity.com/paper/2308.13679/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 25 Aug 2023.

## Builds on (most cited first, 1 of 1)

- [Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data](https://mlsanity.com/paper/1901.04846/) (2019)

## Built on by (most cited first, 6 of 6)

- [Weight Copy and Low-Rank Adaptation for Few-Shot Distillation of Vision Transformers](https://mlsanity.com/paper/2404.09326/) (2024)
- [Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning](https://mlsanity.com/paper/2310.16210/) (2023)
- [Deep Learning for In-Orbit Cloud Segmentation and Classification in Hyperspectral Satellite Data](https://mlsanity.com/paper/2403.08695/) (2024)
- [Data-Efficient Spectral Classification of Hyperspectral Data Using MiniROCKET and HDC-MiniROCKET](https://mlsanity.com/paper/2509.13809/) (2025)
- [Lightweight Cloud Masking Models for On-Board Inference in Hyperspectral Imaging](https://mlsanity.com/paper/2507.08052/) (2025)
- [Curriculum Multi-Task Self-Supervision Improves Lightweight Architectures for Onboard Satellite Hyperspectral Image Segmentation](https://mlsanity.com/paper/2509.13229/) (2025)

## Abstract (arXiv)

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/ .