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<enttypds>Unidad de Planificación Rural Agropecuaria - UPRA</enttypds>
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<attrdefs>Unidad de Planificación Rural Agropecuaria - UPRA</attrdefs>
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<idAbs>&lt;DIV STYLE="text-align:Left;"&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;P&gt;&lt;SPAN&gt;El gobierno nacional, a través de la Unidad de Planificación Rural Agropecuaria (UPRA), busca promover el uso eficiente del suelo y los recursos naturales mediante el diseño de instrumentos a escalas semidetalladas que identifiquen los suelos con capacidad productiva en el marco del concepto de crecimiento verde (UPRA, 2016). En este contexto, y considerando la importancia de los cultivos y su relevancia en Colombia, se ha implementado un sistema de monitoreo para este cultivo.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;El desarrollo metodológico de este proyecto incluye el diseño e implementación de un sistema de monitoreo satelital de coberturas para la detección de áreas productivas del cultivo de maíz industrial a nivel nacional en Colombia. Este sistema se basa en el procesamiento de imágenes satelitales ópticas y de radar, utilizando datos de Sentinel 1 y Sentinel 2. Mediante el entrenamiento de algoritmos de clasificación de inteligencia artificial con procesamiento en la nube, se cuantifica el área productiva y su ubicación espacial. La información principal utilizada incluye límites departamentales, municipales y veredales, y cartografía básica a escala 1:25.000.Esta capa delimita semestralmente las áreas agrícolas en estado productivo del cultivo de maíz industrial, utilizando un enfoque basado en monitoreo satelital de coberturas, procesamiento en la nube y algoritmos de inteligencia artificial. &lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;Dichas zonas se identifican por presentar alto contenido de biomasa vegetal y vigor foliar, así como dinámicas temporales y geográficas asociadas al manejo agrícola. El objetivo es proporcionar información espacial crítica sobre la ubicación, extensión y cambios en las áreas productivas del cultivo de maíz industrial, apoyando así la toma de decisiones y la planificación de políticas agrícolas efectivas.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;Con lo anterior, se obtiene como producto final seis capas geográficas de tipo polígono del monitoreo del cultivo de maíz industrial a nivel nacional para los años 2021, 2022 y 2023, que contiene información de ubicación municipal y departamental, el intervalo de análisis se realiza semestralmente y tres capas geográficas de cambios entre los tres periodos obtenidos en el año a través de una diferencia asimétrica.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;Categorías:- Aumento: Los polígonos adicionales entre el periodo 1 y el periodo 2, se clasifican como aumento.- Disminución: Los polígonos que desaparecen entre el periodo 1 y el periodo 2, se clasifican como disminución.&lt;/SPAN&gt;&lt;/P&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;</idAbs>
<idPurp>El objetivo del producto es reportar la delimitación de las áreas productivas de maíz para el primer semestre de 2022, determinadas a partir de la detección mediante monitoreo satelital, inteligencia artificial y procesamiento en la nube. Esta capa vectorial permite conocer la ubicación espacial, la extensión y los cambios en las áreas productivas a lo largo del tiempo, con el fin de apoyar las políticas institucionales. </idPurp>
<idCredit>Instituto Geográfico Agustín Codazzi (IGAC). (2012). Cartografía básica, escala 1:100.000.
Unidad de Planificación Rural Agropecuaria (UPRA). (2024). Monitoreo del cultivo de maíz de tipo industrial a nivel nacional para el periodo 2021-2023, escala 1:25.000</idCredit>
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En el proceso de preparación de los datos para entrenamiento de modelos Machine Learning – ML y Deep Learning – DL, es necesario dividir las muestras en datos de entrenamiento, validación y evaluación. Las dos primeras particiones son usadas para el entrenamiento y ajuste de hiperparámetros del modelo a utilizar. La partición restante, es usada para estimar la exactitud real de los resultados arrojados por el modelo en comparación con los datos que no fueron usados durante el entrenamiento. Debido a que la toma de muestras tiene en cuenta el contexto de píxel o parche, es recomendado que la estrategia de partición tenga un componente espacial; siguiendo los lineamientos aplicados en investigaciones anteriores como, por ejemplo: Coca-Castro et al., 2019; Rußwurm &amp; Körner, 2018. Por lo tanto, para la generación de muestras o Assets de entrenamiento, se generó una grilla de bloques sobre cada zona de estudio con el fin de extraer dichas áreas de entrenamiento, validación y evaluación.
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