LUCAS Soil Texture Processing Scripts

This repository is a placeholder for the processing scripts of the soil texture data included into the LUCAS dataset [5]. This script is used for a study [3, 4] of 1D Convolutional Neural Networks (CNNs). The code of the 1D CNNs is published in [2].

We can not guarantee completeness or correctness of the code. If you find bugs or if you have suggestions on how to improve the code, we encourage you to post your ideas as GitHub issue.

License:	3-Clause BSD license
Author:	Felix M. Riese
Requirements:	Python 3 with these packages
Citation:	see Citation and in the bibtex file

Notebooks

1. Process LUCAS dataset
2. Plot soil triangle
3. PLot results

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Citation

Code for the Scripts:

[1] F. M. Riese, "LUCAS Soil Texture Processing Scripts," Zenodo, 2020. DOI:0.5281/zenodo.3871431

@misc{riese2020lucas,
    author = {Riese, Felix~M.},
    title = {{LUCAS Soil Texture Processing Scripts}},
    year = {2020},
    DOI = {10.5281/zenodo.3871431},
    publisher = {Zenodo}
}

Code for the 1D CNNs:

[2] F. M. Riese, "CNN Soil Texture Classification", Zenodo, 2019. DOI:10.5281/zenodo.2540718

@misc{riese2019cnn,
    author = {Riese, Felix~M.},
    title = {{CNN Soil Texture Classification}},
    year = {2019},
    DOI = {10.5281/zenodo.2540718},
    publisher = {Zenodo},
}

Code is Supplementary Material to

[3] F. M. Riese and S. Keller, "Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data", ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. IV-2/W5, pp. 615-621, 2019. DOI:10.5194/isprs-annals-IV-2-W5-615-2019

[4] Felix M. Riese. "Development and Applications of Machine Learning Methods for Hyperspectral Data." PhD thesis. Karlsruhe, Germany: Karlsruhe Institute of Technology (KIT), 2020. DOI:10.5445/IR/1000120067

Further References

[5] G. Tóth, A. Jones, and L. Montanarella, "LUCAS Topsoil Survey: Methodology, Data, and Results." Tech. rep. JRC83529. Joint Research Centre of the European Commission, 2013. DOI:10.2788/97922