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TDX (Tesis Doctorals en Xarxa)
Doctoral thesis . 2023
License: CC BY NC SA
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Recolector de Ciencia Abierta, RECOLECTA
Doctoral thesis . 2023
License: CC BY NC SA
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Recolector de Ciencia Abierta, RECOLECTA
Doctoral thesis . 2023
License: CC BY NC SA
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Diseño de arquitecturas de aprendizaje profundo para la determinación de cubiertas sobre el territorio y el estudio de series temporales

Authors: García Rodríguez, Carlos;

Diseño de arquitecturas de aprendizaje profundo para la determinación de cubiertas sobre el territorio y el estudio de series temporales

Abstract

[spa] Las cubiertas del suelo son el resultado de factores naturales, socioeconómicos y de su utilización por parte de las personas en el tiempo y en el espacio. La información sobre las dinámicas del territorio es esencial para la selección, planificación y gestión territorial. La caracterización del tipo de cubierta del suelo y de su uso es clave para muchas aplicaciones: monitorización del medio ambiente, silvicultura, hidrología, agricultura y geología, entre otras. La información obtenida a través del análisis de series temporales de imágenes aerotransportadas y satelitales permite identificar dinámicas de la cobertura del territorio. Con el desarrollo de métodos de clasificación de cubiertas basados en la teledetección es posible evaluar los atributos estáticos y dinámicos de la ocupación temporal del suelo de grandes y pequeñas regiones del territorio, proporcionando información valiosa para la gestión territorial. Actualmente estas tareas se realizan a nivel de fotointerpretación, con altos costes en términos de tiempo y recursos, no solo para su creación sino también para su actualización. La disponibilidad de imágenes detalladas a lo largo del tiempo de las misiones Sentinel-1 y Sentinel-2 del programa espacial europeo Copernicus, y las imágenes aéreas capturadas por el lnstitut Cartografíe i Geologic de Catalunya (ICGC), brindan acceso a una gran cantidad de datos de alta calidad de observación de la tierra. El proyecto de investigación realizado en este doctorado industrial busca desarrollar técnicas de análisis de estos grandes volúmenes de datos para la determinación de usos y coberturas del suelo de forma automática. Se combina una clasificación automática con metodologías de humano en el bucle. De entre las múltiples cubiertas, se presta especial atención a las zonas de extracción de agua de los acuíferos analizando las series temporales de movimientos del suelo con técnicas DlnSAR (lnterferometría Diferencial con Radar de Apertura Sintética).

[eng] Land covers result from natural and socioeconomic factors and their use by people over time and space. lnformation on land dynamics is essential for land selection, planning, and territorial management. Characterizing land cover type and land use is key to many applications, such as environmental monitoring, forestry, hydrology, agriculture and geology, among others. lnformation obtained through time series airborne and satellite imagery analysis allows for identifying land cover states and dynamics. With land cover classification methods based on remote sensing development, it is possible to evaluate the static and dynamic attributes of land cover over time for large and small regions of the territory, providing valuable information far land management. Currently, these tasks are performed at the photo-interpretation level, with very high costs in the farm of time and resources for their creation and updating. The availability of detailed images over time from the Sentinel-1 and Sentinel-2 missions of the European Space program Copernicus and the aerial images captured by the lnstitut Cartografíe i Geologic de Catalunya (ICGC) provides access to a large amount of high-quality earth observation data. The research project carried out in this industrial doctorate seeks, on the one hand, to develop techniques for analyzing large volumes of data for determining land use and land cover automatically. Automatic classification is combined with human-in-the-loop methodologies. Among the multiple covers, special attention is paid to the areas of water extraction from aquifers by analyzing the time series of soil movements with DlnSAR (Differential lnterferometric Synthetic Aperture Radar) techniques.

Programa de Doctorat en Matemàtica i Informàtica

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Spain
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Keywords

62, Teledetecció, Remote-sensing images, Análisis de series temporales, Anàlisi de sèries temporals, Soil surveys, Remote sensing, Aprendizaje automático, Reconocimiento de suelos, Ciències Experimentals i Matemàtiques, Imágenes satelitales, Reconeixement de sòls, Time-series analysis, Aprenentatge automàtic, Machine learning, Teledetección, Imatges satel·litàries

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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