Emilio Guirado Hernández

Investigador Postdoctoral

Emilio Guirado

Obtuve el Máster en Agua y Medio Ambiente en Zonas Áridas y el Doctorado en Ciencias Ambientales por la Universidad de Almería (Almería, España), en 2013 y 2019, respectivamente.

He sido contratado en la UAL/CAESCG, Postdoc en la Universidad de Granada en el departamento de Inteligencia Artificial y Ciencias de la Computación, en University of Western Brittany in AMURE centre, y en la Universidad de Alicante en el IMEM.

Actualmente soy investigador postdoctoral en el Instituto Multidisciplinar para el Estudio del Medio Ambiente (IMEM), de la Universidad de Alicante (Alicante, España).

INVESTIGACIÓN

Investigo el funcionamiento de los ecosistemas y la biodiversidad con la teledetección y la perspectiva del deep learning.

LÍNEAS DE INVESTIGACIÓN

  • Bosques
  • Zonas áridas
  • Modelos espaciales
  • Deep learning
  • Remote sensing

Publicaciones

Climate legacies drive the distribution and future restoration potential of dryland forests The potential of groundwater-dependent ecosystems to enhance soil biological activity and soil fertility in drylands TimeSpec4LULC: a global multispectral time series database for training LULC mapping models with machine learning Phylotype diversity within soil fungal functional groups drives ecosystem stability Introduction: Drylands. Opportunities, challenges, and threats Humidity and low pH boost occurrence of Onygenales fungi in soil at global scale Desertification in Spain: A Sound Diagnosis without Solutions and New Scenarios Temperature thresholds drive the global distribution of soil fungal decomposers Desertification in Spain: a sound diagnosis unfollowed by solutions Effects of climate change and land use intensification on regional biological soil crust cover and composition in southern Africa Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors. ¿Se puede cartografiar la desertificación? Luces y sombras de una tarea desafiante Potencial de la inteligencia artificial para avanzar en el estudio de la desertificación Sentinel2GlobalLULC: A deep-learning-ready Sentinel-2 RGB image dataset for global land use/cover mapping Aridity Thresholds Determine the Relationships Between Ecosystem Functioning and Remotely Sensed Indicators Across Patagonia Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN Mediterranean Landscape Re-Greening at the Expense of South American Agricultural Expansion A remote sensing-based dataset to characterize the ecosystem functioning and functional diversity of a Biosphere Reserve: Sierra Nevada (SE Spain) Impulsando estrategias colectivas ciencia-gestión-sociedad para conservar el hábitat de Ziziphus lotus (Hábitat Prioritario 5220) MONOGRÁFICO: Interfaz ciencia-gestión-sociedad en el ámbito de la conservación: avances conceptuales y metodológicos COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images Unraveling Misunderstandings about Desertification: The Paradoxical Case of the Tabernas-Sorbas Basin in Southeast Spain Discarded food and resource depletion FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformations Desertifying deserts Tree Cover Estimation in Global Drylands from Space Using Deep Learning Modeling carbon dioxide for show cave conservation A multi-temporal object-based image analysis to detect long-lived shrub cover changes in drylands Whale counting in satellite and aerial images with deep learning Remote sensing-derived fractures and shrub patterns to identify groundwater dependence Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery. Assessment of multiresolution segmentation for extracting greenhouses from worldview-2 imagery De dónde viene el agua del Manantial de Los Molinos? Calculating the maximum visitability in tourist caves by CAVIX method: El Soplao (Cantabria, Spain) Detecting gypsum caves with microgravity and ERT under soil water content variations (Sorbas, SE Spain)
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