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Resources","sub_category":"Soil and Land","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"[![Build Status](https://travis-ci.com/felixriese/CNN-SoilTextureClassification.svg?branch=master)](https://travis-ci.com/felixriese/CNN-SoilTextureClassification)\n[![codecov](https://codecov.io/gh/felixriese/CNN-SoilTextureClassification/branch/master/graph/badge.svg)](https://codecov.io/gh/felixriese/CNN-SoilTextureClassification)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/01c84806115646eb9ba2dde39a84822e)](https://www.codacy.com/manual/felixriese/CNN-SoilTextureClassification?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=felixriese/CNN-SoilTextureClassification\u0026amp;utm_campaign=Badge_Grade)\n[![Paper](https://img.shields.io/badge/DOI-10.5194%2Fisprs--annals--IV--2--W5--615--2019-blue)](https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W5/615/2019/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)\n\n# CNN Soil Texture Classification\n\n1-dimensional convolutional neural networks (CNN) for the classification of\nsoil texture based on hyperspectral data.\n\n## Description\n\nWe present 1-dimensional (1D) convolutional neural networks (CNN) for the\nclassification of soil texture based on hyperspectral data. The following CNN\nmodels are included:\n\n* `LucasCNN`\n* `LucasResNet`\n* `LucasCoordConv`\n* `HuEtAl`: 1D CNN by Hu et al. (2015), DOI: [10.1155/2015/258619](http://dx.doi.org/10.1155/2015/258619)\n* `LiuEtAl`: 1D CNN by Liu et al. (2018), DOI: [10.3390/s18093169](https://dx.doi.org/10.3390%2Fs18093169)\n\nThese 1D CNNs are optimized for the soil texture classification based on the hyperspectral data of the *Land Use/Cover Area Frame Survey* (LUCAS) topsoil dataset. It is available [here](https://esdac.jrc.ec.europa.eu/projects/lucas). For more information have a look in our publication (see below).\n\n**Introducing paper:** [arXiv:1901.04846](https://arxiv.org/abs/1901.04846)\n\n**Licence:** [MIT](LICENSE)\n\n**Authors:**\n\n* [Felix M. Riese](mailto:felix.riese@kit.edu)\n* [Sina Keller](mailto:sina.keller@kit.edu)\n\n**Citation of the code and the paper:** see [below](#citation) and in the [bibtex](bibliography.bib) file\n\n## Requirements\n\n* see [Dockerfile](Dockerfile)\n* download `coord.py` from [titu1994/keras-coordconv](https://github.com/titu1994/keras-coordconv) based on [arXiv:1807.03247](https://arxiv.org/abs/1807.03247)\n\n## Setup\n\n```bash\ngit clone https://github.com/felixriese/CNN-SoilTextureClassification.git\n\ncd CNN-SoilTextureClassification/\n\nwget https://raw.githubusercontent.com/titu1994/keras-coordconv/c045e3f1ff7dabd4060f515e4b900263eddf1723/coord.py .\n```\n\n## Usage\n\nYou can import the Keras models like that:\n\n```python\nimport cnn_models as cnn\n\nmodel = cnn.getKerasModel(\"LucasCNN\")\nmodel.compile(...)\n\n```\n\nExample code is given in the `lucas_classification.py`. You can use it like that:\n\n```python\nfrom lucas_classification import lucas_classification\n\nscore = lucas_classification(\n    data=[X_train, X_val, y_train, y_val],\n    model_name=\"LucasCNN\",\n    batch_size=32,\n    epochs=200,\n    random_state=42)\n\nprint(score)\n```\n\n## Citation\n\n[1] F. M. Riese, \"CNN Soil Texture Classification\",\n[DOI:10.5281/zenodo.2540718](https://doi.org/10.5281/zenodo.2540718), 2019.\n\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2540718.svg)](https://doi.org/10.5281/zenodo.2540718)\n\n```tex\n@misc{riese2019cnn,\n    author       = {Riese, Felix~M.},\n    title        = {{CNN Soil Texture Classification}},\n    year         = {2019},\n    publisher    = {Zenodo},\n    DOI          = {10.5281/zenodo.2540718},\n}\n```\n\n## Code is Supplementary Material to\n\n[2] F. M. Riese and S. Keller, \"Soil Texture Classification with 1D\nConvolutional Neural Networks based on Hyperspectral Data\", ISPRS Annals of\nPhotogrammetry, Remote Sensing and Spatial Information Sciences, vol. IV-2/W5,\npp. 615-621, 2019. [DOI:10.5194/isprs-annals-IV-2-W5-615-2019](https://doi.org/10.5194/isprs-annals-IV-2-W5-615-2019)\n\n```tex\n@article{riese2019soil,\n    author = {Riese, Felix~M. and Keller, Sina},\n    title = {Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data},\n    year = {2019},\n    journal = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},\n    volume = {IV-2/W5},\n    pages = {615--621},\n    doi = {10.5194/isprs-annals-IV-2-W5-615-2019},\n}\n```\n\n[3] F. M. Riese, \"LUCAS Soil Texture Processing Scripts,\" Zenodo, 2020.\n[DOI:0.5281/zenodo.3871431](https://doi.org/10.5281/zenodo.3871431)\n\n[4] Felix M. Riese. \"Development and Applications of Machine Learning Methods\nfor Hyperspectral Data.\" PhD thesis. Karlsruhe, Germany: Karlsruhe Institute of\nTechnology (KIT), 2020. 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