<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2602-8484</journal-id>
<journal-title><![CDATA[FIGEMPA: Investigación y Desarrollo]]></journal-title>
<abbrev-journal-title><![CDATA[Figempa]]></abbrev-journal-title>
<issn>2602-8484</issn>
<publisher>
<publisher-name><![CDATA[Universidad Central de Ecuador ]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2602-84842022000300048</article-id>
<article-id pub-id-type="doi">10.29166/revfig.v13i1.3121</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Delimitación automática de ceniza volcánica en imágenes satelitales mediante Deep Learning]]></article-title>
<article-title xml:lang=""><![CDATA[Automatic delimitation of volcanic ash in satellite images using Deep Learning]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aldás Núñez]]></surname>
<given-names><![CDATA[Roberth Joel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Tuz Chamorro]]></surname>
<given-names><![CDATA[Katherin Vanessa]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vega Ocaña]]></surname>
<given-names><![CDATA[Jair Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Velasco Haro]]></surname>
<given-names><![CDATA[Marco Sebastián]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mejía Escobar]]></surname>
<given-names><![CDATA[Christian Iván]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Central del Ecuador  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Central del Ecuador  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Central del Ecuador  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad Central del Ecuador  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Universidad Central del Ecuador  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<volume>13</volume>
<numero>1</numero>
<fpage>48</fpage>
<lpage>58</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_arttext&amp;pid=S2602-84842022000300048&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_abstract&amp;pid=S2602-84842022000300048&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_pdf&amp;pid=S2602-84842022000300048&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  La Inteligencia Artificial ha tenido un gran impacto en los últimos años, cada vez este campo de la Informática es más utilizado en el tratamiento de problemas en el área geológica. Una de las principales aplicaciones es la detección y segmentación de ceniza en imágenes satelitales. Para tal fin, proponemos un modelo de Deep Learning basado en una red neuronal convolucional (CNN) y entrenado con un dataset de imágenes satelitales que tienen aplicado el filtro &#8220;ash&#8221;, que proporciona una coloración rosada rojiza a la ceniza, facilitando el proceso de segmentación. Los resultados obtenidos indican una precisión del 99%, conveniente para su aplicación práctica para la segmentación de la ceniza emitida por el Volcán Sangay, el cual ha presentado periodos de actividad volcánica en los últimos años. Las imágenes segmentadas generadas por nuestro modelo son congruentes con los estudios publicados por el IG-EPN.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Artificial Intelligence has had a big impact in recent years, this field of Informatics is increasingly used to solve geological problems. One of the main applications is the detection and segmentation of volcanic ash in satellite images. For this purpose, we propose a Deep Learning model based on a Convolutional Neural Network (CNN), trained with a satellite image dataset where the "ash" filter is applied, which provides a reddish-pink coloration to the ash, facilitating the segmentation process. The results show an accuracy of 99%, which is suitable for the segmentation of the ash emitted by Sangay Volcano, which has presented periods of volcanic activity in recent years. Our model generated segmented images that are consistent with the studies published by the IG-EPN.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Deep Learning]]></kwd>
<kwd lng="es"><![CDATA[Segmentación de ceniza]]></kwd>
<kwd lng="es"><![CDATA[Red neuronal convolucional]]></kwd>
<kwd lng="es"><![CDATA[Imágenes Satelitales]]></kwd>
<kwd lng="es"><![CDATA[Volcán Sangay]]></kwd>
<kwd lng="en"><![CDATA[Deep Learning]]></kwd>
<kwd lng="en"><![CDATA[Ash segmentation]]></kwd>
<kwd lng="en"><![CDATA[Convolutional neuronal network]]></kwd>
<kwd lng="en"><![CDATA[Satellite Images]]></kwd>
<kwd lng="en"><![CDATA[Sangay Volcano]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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