Miguel Angel Manso Callejo - Dissemin

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All papers authored by Miguel Angel Manso Callejo

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2022 Calimanut-Ionut Cira

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, Martin Kada, Miguel-Ángel Manso-Callejo

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, Ramón Alcarria

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, Borja Bordel Sanchez

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Improving Road Surface Area Extraction via Semantic Segmentation with Conditional Generative Learning for Deep Inpainting Operations

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2021 Calimanut-Ionut Cira

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, Miguel-Ángel Manso-Callejo

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, Ramón Alcarria

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, Teresa Fernández Pareja

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, Borja Bordel Sánchez

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, Francisco Serradilla

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Generative Learning for Postprocessing Semantic Segmentation Predictions: A Lightweight Conditional Generative Adversarial Network Based on Pix2pix to Improve the Extraction of Road Surface Areas

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Miguel-Angel Manso-Callejo

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, Calimanut-Ionut Cira

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, Ramon Pablo Alcarria Garrido

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, Francisco Javier Gonzalez Matesanz

First Dataset of Wind Turbine Data Created at National Level With Deep Learning Techniques From Aerial Orthophotographs With a Spatial Resolution of 0.5 M/Pixel

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2020 Miguel-Ángel Manso-Callejo

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, Calimanut-Ionut Cira

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, Ramón Alcarria

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, José-Juan Arranz-Justel

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Optimizing the Recognition and Feature Extraction of Wind Turbines through Hybrid Semantic Segmentation Architectures

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Calimanut-Ionut Cira

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, Ramón Alcarria

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, Miguel-Ángel Manso-Callejo

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, Francisco Serradilla

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A Deep Learning-Based Solution for Large-Scale Extraction of the Secondary Road Network from High-Resolution Aerial Orthoimagery

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Calimanut-Ionut Cira

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, Ramon Alcarria

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, Miguel-Ángel Manso-Callejo

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, Francisco Serradilla

A Framework Based on Nesting of Convolutional Neural Networks to Classify Secondary Roads in High Resolution Aerial Orthoimages

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2019 Calimanut-Ionut Cira

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, Ramón Alcarria

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, Miguel-Ángel Manso-Callejo

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, Francisco Serradilla

A Deep Convolutional Neural Network to Detect the Existence of Geospatial Elements in High-Resolution Aerial Imagery

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This paper was not found in any repository, but could be made available legally by the author.

Calimanut-Ionut Cira

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, Ramon Alcarria

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, Miguel-Ángel Manso-Callejo

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, Francisco Serradilla

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Evaluation of Transfer Learning Techniques with Convolutional Neural Networks (CNNs) to Detect the Existence of Roads in High-Resolution Aerial Imagery

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