Datasets:
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0ang20190922t192642_r0_c256_w512_h512
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1ang20190922t192642_r10240_c0_w512_h512
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2ang20190922t192642_r10752_c0_w512_h512
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3ang20190922t192642_r2048_c0_w512_h512
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4ang20190922t192642_r3270_c384_w151_h151
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5ang20190922t192642_r4034_c360_w151_h151
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6ang20190922t192642_r4578_c217_w151_h151
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7ang20190922t192642_r4834_c257_w151_h151
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8ang20190922t192642_r4928_c373_w151_h151
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9ang20190922t192642_r512_c256_w512_h512
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10ang20190922t192642_r6423_c113_w151_h151
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11ang20190922t192642_r7680_c0_w512_h512
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12ang20190922t192642_r9472_c0_w512_h512
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13ang20190922t194340_r0_c256_w512_h512
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14ang20190922t194340_r1953_c438_w151_h151
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15ang20190922t194340_r2642_c324_w151_h151
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16ang20190922t194340_r3328_c0_w512_h512
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17ang20190922t194340_r5074_c444_w151_h151
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18ang20190922t194340_r6144_c0_w512_h512
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19ang20190922t194340_r7936_c0_w512_h512
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20ang20190922t194340_r8448_c0_w512_h512
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21ang20190922t194340_r9472_c0_w512_h512
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22ang20190922t194340_r9984_c0_w512_h512
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23ang20190922t203229_r10752_c512_w512_h512
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24ang20190922t203229_r1280_c768_w512_h512
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25ang20190922t203229_r256_c512_w512_h512
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26ang20190922t203229_r3328_c512_w512_h512
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27ang20190922t203229_r5632_c256_w512_h512
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28ang20190922t203229_r7371_c312_w151_h151
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29ang20190922t203229_r7396_c411_w151_h151
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30ang20190922t203229_r7486_c72_w151_h151
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31ang20190922t203229_r768_c512_w512_h512
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32ang20190922t203229_r8192_c256_w512_h512
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33ang20190923t163307_r0_c0_w512_h512
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34ang20190923t163307_r1024_c0_w512_h512
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35ang20190923t163307_r10752_c0_w512_h512
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36ang20190923t163307_r11597_c539_w151_h151
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37ang20190923t163307_r12822_c605_w151_h151
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38ang20190923t163307_r13596_c639_w151_h151
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39ang20190923t163307_r13801_c577_w151_h151
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40ang20190923t163307_r1536_c0_w512_h512
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41ang20190923t163307_r2796_c346_w151_h151
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42ang20190923t163307_r4113_c198_w151_h151
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43ang20190923t163307_r4370_c239_w151_h151
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44ang20190923t163307_r5120_c256_w512_h512
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45ang20190923t163307_r5958_c123_w151_h151
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46ang20190923t163307_r6778_c561_w151_h151
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47ang20190923t163307_r7168_c256_w512_h512
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48ang20190923t163307_r7936_c256_w512_h512
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49ang20190923t170747_r0_c0_w512_h512
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50ang20190923t170747_r10240_c256_w512_h512
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51ang20190923t170747_r10752_c256_w512_h512
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52ang20190923t170747_r12288_c256_w512_h512
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53ang20190923t170747_r14210_c160_w151_h151
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54ang20190923t170747_r15616_c256_w512_h512
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55ang20190923t170747_r2291_c472_w151_h151
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56ang20190923t170747_r3144_c180_w151_h151
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57ang20190923t170747_r3502_c419_w151_h151
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58ang20190923t170747_r5120_c256_w512_h512
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59ang20190923t170747_r512_c0_w512_h512
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60ang20190923t170747_r6732_c289_w151_h151
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61ang20190923t170747_r8220_c606_w151_h151
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62ang20190923t170747_r8630_c560_w151_h151
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63ang20190923t170747_r9537_c44_w151_h151
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64ang20190923t172416_r0_c0_w512_h512
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65ang20190923t172416_r10579_c413_w151_h151
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66ang20190923t172416_r12122_c231_w151_h151
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67ang20190923t172416_r13312_c256_w512_h512
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68ang20190923t172416_r13824_c256_w512_h512
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69ang20190923t172416_r15067_c399_w151_h151
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70ang20190923t172416_r2048_c0_w512_h512
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71ang20190923t172416_r3584_c0_w512_h512
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72ang20190923t172416_r4608_c0_w512_h512
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73ang20190923t172416_r9472_c0_w512_h512
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74ang20190923t174142_r0_c0_w512_h512
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75ang20190923t174142_r11264_c256_w512_h512
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76ang20190923t174142_r11776_c256_w512_h512
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77ang20190923t174142_r12544_c256_w512_h512
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78ang20190923t174142_r13784_c155_w151_h151
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79ang20190923t174142_r15306_c621_w151_h151
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80ang20190923t174142_r15511_c283_w151_h151
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81ang20190923t174142_r4096_c0_w512_h512
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82ang20190923t174142_r512_c0_w512_h512
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83ang20190923t174142_r5826_c168_w151_h151
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84ang20190923t174142_r7561_c18_w151_h151
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85ang20190923t174142_r7604_c458_w151_h151
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86ang20190923t174142_r7936_c0_w512_h512
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87ang20190923t174142_r9431_c547_w151_h151
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88ang20190923t174142_r9678_c231_w151_h151
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89ang20190923t181729_r0_c256_w512_h512
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90ang20190923t181729_r10752_c0_w512_h512
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91ang20190923t181729_r11776_c0_w512_h512
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92ang20190923t181729_r13645_c146_w151_h151
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93ang20190923t181729_r1984_c475_w151_h151
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94ang20190923t181729_r2423_c148_w151_h151
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95ang20190923t181729_r3963_c475_w151_h151
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96ang20190923t181729_r4864_c256_w512_h512
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97ang20190923t181729_r512_c256_w512_h512
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98ang20190923t181729_r6905_c570_w151_h151
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99ang20190923t181729_r8192_c256_w512_h512
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Paper
Overview
This repository hosts curated data products used for evaluation and training in the article "Optimizing Methane Detection Onboard Satellites: Speed, Accuracy, and Low-Power Solutions for Resource-Constrained Hardware."
The dataset consists exclusively of products generated from 72 hyperspectral channels within the wavelength range of 2122–2488 nm. These products served as the training data for the models available here.
The original raw hyperspectral data required to create these products is publicly accessible at: https://huggingface.co/collections/previtus/starcop-67f13cf30def71591f281a41
Sample Usage (Notebook Demos)
You can try out our demos directly in Google Colab:
Models Demo
Demonstrates model inference.
Products Creation and Benchmarking Demo
Demonstrates generating products and measuring their runtime.
Citation
If you use this dataset in your research, please cite our article:
@misc{herec2025optimizingmethanedetectionboard,
title={Optimizing Methane Detection On Board Satellites: Speed, Accuracy, and Low-Power Solutions for Resource-Constrained Hardware},
author={Jonáš Herec and Vít Růžička and Rado Pitoňák},
year={2025},
eprint={2507.01472},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2507.01472},
}
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