{"data":{"id":"10.5281/zenodo.7806929","type":"dois","attributes":{"doi":"10.5281/zenodo.7806929","prefix":"10.5281","suffix":"zenodo.7806929","identifiers":[{"identifier":"https://zenodo.org/record/7806930","identifierType":"URL"}],"alternateIdentifiers":[{"alternateIdentifierType":"URL","alternateIdentifier":"https://zenodo.org/record/7806930"}],"creators":[{"name":"Vernikos, Ioannis","nameType":"Personal","givenName":"Ioannis","familyName":"Vernikos","affiliation":["IIT/ NCSR Demokritos , Greece"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1554-0539","nameIdentifierScheme":"ORCID"}]},{"name":"Giannopoulos, Georgios","nameType":"Personal","givenName":"Georgios","familyName":"Giannopoulos","affiliation":["School of Rural, Surveying and Geo-Informatics Engineering/ NTUA, Greece"],"nameIdentifiers":[]},{"name":"Christopoulou, Aikaterini","nameType":"Personal","givenName":"Aikaterini","familyName":"Christopoulou","affiliation":["IIT/ NCSR Demokritos , Greece"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9928-141X","nameIdentifierScheme":"ORCID"}]},{"name":"Anxhelo Begaj","affiliation":["IIT/ NCSR Demokritos , Greece"],"nameIdentifiers":[]},{"name":"Stefouli, Marianthi","nameType":"Personal","givenName":"Marianthi","familyName":"Stefouli","affiliation":["IIT/ NCSR Demokritos , Greece"],"nameIdentifiers":[]},{"name":"Bratsolis, Emmanuel","nameType":"Personal","givenName":"Emmanuel","familyName":"Bratsolis","affiliation":["IIT/ NCSR Demokritos , Greece"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2171-9359","nameIdentifierScheme":"ORCID"}]},{"name":"Charou, Eleni","nameType":"Personal","givenName":"Eleni","familyName":"Charou","affiliation":["IIT/ NCSR Demokritos , Greece"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0452-1754","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"A dataset of Earth Observation Data for Lithological Mapping using Machine Learning"}],"publisher":"Zenodo","container":{"type":"DataRepository","identifier":"https://zenodo.org/communities/gnss-for-geodesy-positioning-navigation","identifierType":"URL"},"publicationYear":2023,"subjects":[{"subject":"Remote Sensing"},{"subject":"Greece"},{"subject":"Aster"},{"subject":"Sentinel"},{"subject":"Lithology"},{"subject":"Earth Observation"},{"subject":"Copernicus"}],"contributors":[],"dates":[{"date":"2023-01-27","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsDescribedBy","relatedIdentifier":"10.5194/egusphere-egu23-17570","resourceTypeGeneral":"ConferencePaper","relatedIdentifierType":"DOI"},{"relationType":"HasVersion","relatedIdentifier":"10.5281/zenodo.7806930","relatedIdentifierType":"DOI"},{"relationType":"IsPartOf","relatedIdentifier":"https://zenodo.org/communities/gnss-for-geodesy-positioning-navigation","relatedIdentifierType":"URL"},{"relationType":"IsPartOf","relatedIdentifier":"https://zenodo.org/communities/photogrammetry","relatedIdentifierType":"URL"},{"relationType":"IsPartOf","relatedIdentifier":"https://zenodo.org/communities/remote-sensing","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1.0.0","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"},{"rights":"Open Access","rightsUri":"info:eu-repo/semantics/openAccess"}],"descriptions":[{"description":"\u003cstrong\u003eDataset Information\u003c/strong\u003e Machine Learning (ML) algorithms had successfully contributed in the creation of automated methods of recognizing patterns in high-dimensional data. Remote sensing data covers wide geographical areas and could be used to solve the problem of the demand of various in-situ data. Lithologicall mapping using remotely sensed data is one of the most challenging applications of ML algorithms. In the framework of the “AI for Geoapplications” project , ML and especially Deep Learning (DL) methodologies are investigated for the identification and characterization of the lithology based on remote sensing data in various pilot areas in Greece. In order to train and test the various ML algorithms, a dataset consisting of 30 ROIs selected mainly from low -vegetated areas, that cover 2% of the total area of Greece was created \u003cstrong\u003eDataset Preprocessing\u003c/strong\u003e Dataset preprocessing was executed using a combination of SNAP, QGIS and ENVI tools. Preprocessing steps: Defining areas with the following properties: Zero cloud and snow coverage No water bodies Minimum vegetation For the Aster Images: Subset on defined areas Mosaic images when needed Digitising clouds For the Labels: We got the Soil map from YPEN (https://ypen.gov.gr/) Subset on defined areas All categories are represented with good analogies Clip label files with digitised clouds Rasterize For the Labels we have eighteen categories for the twenty-eight areas that we collected data. We use the following coding for the Labels of our \u003cstrong\u003eDataset\u003c/strong\u003e: \u003cstrong\u003eAlluvial deposits\u003c/strong\u003e \u003cstrong\u003e0\u003c/strong\u003e \u003cstrong\u003eLimestone colluvial deposits\u003c/strong\u003e \u003cstrong\u003e1\u003c/strong\u003e \u003cstrong\u003eLimestones\u003c/strong\u003e \u003cstrong\u003e2\u003c/strong\u003e \u003cstrong\u003eSchists\u003c/strong\u003e \u003cstrong\u003e3\u003c/strong\u003e \u003cstrong\u003eQuaternary sediments\u003c/strong\u003e \u003cstrong\u003e4\u003c/strong\u003e \u003cstrong\u003eGneiss\u003c/strong\u003e \u003cstrong\u003e5\u003c/strong\u003e \u003cstrong\u003eSlope fan debris\u003c/strong\u003e \u003cstrong\u003e6\u003c/strong\u003e \u003cstrong\u003eMixed flysch\u003c/strong\u003e \u003cstrong\u003e7\u003c/strong\u003e \u003cstrong\u003eFlysch shale and cherts\u003c/strong\u003e \u003cstrong\u003e8\u003c/strong\u003e \u003cstrong\u003eDolomites\u003c/strong\u003e \u003cstrong\u003e9\u003c/strong\u003e \u003cstrong\u003eGranite\u003c/strong\u003e \u003cstrong\u003e10\u003c/strong\u003e \u003cstrong\u003eSandstone flysch\u003c/strong\u003e \u003cstrong\u003e11\u003c/strong\u003e \u003cstrong\u003eFlysch colluvial deposits\u003c/strong\u003e \u003cstrong\u003e12\u003c/strong\u003e \u003cstrong\u003ePeridotite and Gabbro\u003c/strong\u003e \u003cstrong\u003e13\u003c/strong\u003e \u003cstrong\u003eRiver bed deposits\u003c/strong\u003e \u003cstrong\u003e14\u003c/strong\u003e \u003cstrong\u003eGneiss colluvial deposits\u003c/strong\u003e \u003cstrong\u003e15\u003c/strong\u003e \u003cstrong\u003eNot available\u003c/strong\u003e \u003cstrong\u003e-100\u003c/strong\u003e \u003cstrong\u003ecloud coverage\u003c/strong\u003e \u003cstrong\u003e-999\u003c/strong\u003e The following table lists the available \u003cstrong\u003eareas \u003c/strong\u003eand the \u003cstrong\u003ecategories \u003c/strong\u003ethat each contains\u003cstrong\u003e: Lithology_Dataset \u003c/strong\u003e For the \u003cstrong\u003eSentinel-2 images\u003c/strong\u003e, we made the following process: \u003cstrong\u003eResampling 10m\u003c/strong\u003e \u003cstrong\u003eSubset on defined areas\u003c/strong\u003e The Sentinel-2 map contains: Sentinel 2 false colour composite 11/8/4 with OSM background The Final step is the collocation of the previous into a datacube i.e a multidimensional array with 25 bands (datacube dimensions differentiate for every area) using the Aster image as base (15m spatial resolution). Bands 1-14: Aster Bands 15-24: S2 Band 25: Label The code for preprocessing the dataset in order to be used for machine learning algorithms can be found in the following link: https://github.com/georgegiannop/Lithology \u003cstrong\u003eCitation\u003c/strong\u003e If you use this dataset in your work, please cite our paper: Vernikos, I., Giannopoulos, G., Christopoulou, A., Begaj, A., Stefouli, M., Bratsolis, E., and Charou, E.: A dataset of Earth Observation Data for Lithological Mapping using Machine Learning, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17570, https://doi.org/10.5194/egusphere-egu23-17570, 2023.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"<?xml version="1.0" encoding="utf-8"?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/ZENODO.7806929</identifier>
  <creators>
    <creator>
      <creatorName>Ioannis Vernikos</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-1554-0539</nameIdentifier>
      <affiliation>IIT/ NCSR Demokritos , Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Georgios Giannopoulos</creatorName>
      <affiliation>School of Rural, Surveying and Geo-Informatics Engineering/ NTUA, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Aikaterini Christopoulou</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-9928-141X</nameIdentifier>
      <affiliation>IIT/ NCSR Demokritos , Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Anxhelo Begaj</creatorName>
      <affiliation>IIT/ NCSR Demokritos , Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Marianthi Stefouli</creatorName>
      <affiliation>IIT/ NCSR Demokritos , Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Emmanuel Bratsolis</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2171-9359</nameIdentifier>
      <affiliation>IIT/ NCSR Demokritos , Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Eleni Charou</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0452-1754</nameIdentifier>
      <affiliation>IIT/ NCSR Demokritos , Greece</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A dataset of Earth Observation Data for Lithological Mapping using Machine Learning</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2023</publicationYear>
  <subjects>
    <subject>Remote Sensing</subject>
    <subject>Greece</subject>
    <subject>Aster</subject>
    <subject>Sentinel</subject>
    <subject>Lithology</subject>
    <subject>Earth Observation</subject>
    <subject>Copernicus</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2023-01-27</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
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    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/photogrammetry</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/remote-sensing</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;&lt;strong&gt;Dataset Information&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Machine Learning (ML) algorithms had successfully contributed in the creation of automated methods of recognizing patterns in high-dimensional data. Remote sensing data&amp;nbsp; covers&amp;nbsp; wide&amp;nbsp; geographical areas and could be used to solve the problem of the demand of various&amp;nbsp; in-situ data.&amp;nbsp; Lithologicall mapping using remotely sensed data&amp;nbsp; is one of the most challenging&amp;nbsp; applications of ML algorithms. In the framework of the &amp;ldquo;AI for Geoapplications&amp;rdquo; project , ML and especially Deep Learning (DL) methodologies are investigated&amp;nbsp; for&amp;nbsp; the identification and characterization of the lithology based on remote sensing data in various&amp;nbsp; pilot areas&amp;nbsp; in Greece.&amp;nbsp; In order to train and test the various ML algorithms, a dataset consisting of&amp;nbsp; 30 ROIs selected&amp;nbsp; mainly&amp;nbsp; from low -vegetated areas,&amp;nbsp; that cover 2% of the total&amp;nbsp; area of Greece was created&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Dataset Preprocessing&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Dataset preprocessing was executed using a combination of SNAP, QGIS and ENVI tools.&lt;/p&gt;

&lt;p&gt;Preprocessing steps:&lt;/p&gt;

&lt;p&gt;Defining areas with the following properties:&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;
	&lt;p&gt;Zero cloud and snow coverage&lt;/p&gt;
	&lt;/li&gt;
	&lt;li&gt;
	&lt;p&gt;No water bodies&lt;/p&gt;
	&lt;/li&gt;
	&lt;li&gt;
	&lt;p&gt;Minimum vegetation&lt;/p&gt;
	&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;For the Aster Images:&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;
	&lt;p&gt;Subset on defined areas&lt;/p&gt;
	&lt;/li&gt;
	&lt;li&gt;
	&lt;p&gt;Mosaic images when needed&lt;/p&gt;
	&lt;/li&gt;
	&lt;li&gt;
	&lt;p&gt;Digitising clouds&lt;/p&gt;
	&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;For the Labels:&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;
	&lt;p&gt;We got the Soil map from YPEN (&lt;a href="https://ypen.gov.gr/"&gt;https://ypen.gov.gr/&lt;/a&gt;)&lt;/p&gt;
	&lt;/li&gt;
	&lt;li&gt;
	&lt;p&gt;Subset on defined areas&lt;/p&gt;
	&lt;/li&gt;
	&lt;li&gt;
	&lt;p&gt;All categories are represented with good analogies&lt;/p&gt;
	&lt;/li&gt;
	&lt;li&gt;
	&lt;p&gt;Clip label files with digitised clouds&lt;/p&gt;
	&lt;/li&gt;
	&lt;li&gt;
	&lt;p&gt;Rasterize&lt;/p&gt;
	&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;For the Labels we have eighteen categories for the twenty-eight areas that we collected data.&amp;nbsp;We use the following coding&amp;nbsp;for the&amp;nbsp;Labels of our &lt;strong&gt;Dataset&lt;/strong&gt;:&lt;/p&gt;

&lt;table&gt;
	&lt;tbody&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;Alluvial deposits&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;0&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;Limestone colluvial deposits&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;1&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;Limestones&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;2&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;Schists&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;3&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;Quaternary sediments&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;4&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;Gneiss&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;5&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;Slope fan debris&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;6&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;Mixed flysch&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;7&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;Flysch shale and cherts&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;8&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;Dolomites&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;9&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;Granite&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;10&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;Sandstone flysch&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;11&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;Flysch colluvial deposits&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;12&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;Peridotite and Gabbro&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;13&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;River bed deposits&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;14&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;Gneiss colluvial deposits&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;15&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;Not available&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;-100&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;cloud coverage&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
			&lt;td&gt;
			&lt;p&gt;&lt;strong&gt;-999&lt;/strong&gt;&lt;/p&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
	&lt;/tbody&gt;
&lt;/table&gt;

&lt;p&gt;The following table lists the available &lt;strong&gt;areas &lt;/strong&gt;and the &lt;strong&gt;categories &lt;/strong&gt;that each contains&lt;strong&gt;:&amp;nbsp;&lt;a href="https://docs.google.com/spreadsheets/d/17q0L5Ltz7V4uBY9i6DhULJsJCtf7BOY1nbblB-hf3Pw/edit?usp=share_link"&gt;Lithology_Dataset&lt;/a&gt; &lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;For the &lt;strong&gt;Sentinel-2 images&lt;/strong&gt;, we made the following process:&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;
	&lt;p&gt;&lt;strong&gt;Resampling 10m&lt;/strong&gt;&lt;/p&gt;
	&lt;/li&gt;
	&lt;li&gt;
	&lt;p&gt;&lt;strong&gt;Subset on defined areas&lt;/strong&gt;&lt;/p&gt;
	&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The Sentinel-2 map contains: Sentinel 2 false colour composite 11/8/4 with OSM background&lt;/p&gt;

&lt;p&gt;The Final step is the collocation of the previous into a datacube i.e a multidimensional array with 25 bands (datacube dimensions differentiate for every area) using the Aster image as base (15m spatial resolution).&amp;nbsp;&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;
	&lt;p&gt;Bands 1-14: Aster&lt;/p&gt;
	&lt;/li&gt;
	&lt;li&gt;
	&lt;p&gt;Bands 15-24: S2&lt;/p&gt;
	&lt;/li&gt;
	&lt;li&gt;
	&lt;p&gt;Band 25: Label&lt;/p&gt;
	&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The code for preprocessing the dataset in order to be used for machine learning algorithms can be found in the following link:&amp;nbsp;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/georgegiannop/Lithology"&gt;https://github.com/georgegiannop/Lithology&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Citation&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;If you use this dataset in your work, please cite our paper:&lt;/p&gt;

&lt;p&gt;Vernikos, I., Giannopoulos, G., Christopoulou, A., Begaj, A., Stefouli, M., Bratsolis, E., and Charou, E.: A dataset of Earth Observation Data for Lithological Mapping using Machine Learning, EGU General Assembly 2023, Vienna, Austria, 24&amp;ndash;28 Apr 2023, EGU23-17570,&amp;nbsp;&lt;a href="https://doi.org/10.5194/egusphere-egu23-17570"&gt;https://doi.org/10.5194/egusphere-egu23-17570&lt;/a&gt;, 2023.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;</description>
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