Listar por autor "Ayala Lauroba, Christian"
Mostrando ítems 1-9 de 9
-
A deep learning approach to an enhanced building footprint and road detection in high-resolution satellite imagery
Ayala Lauroba, Christian; Sesma Redín, Rubén
; Aranda, Carlos; Galar Idoate, Mikel 
(MDPI, 2021)
Artículo / Artikulua 
The detection of building footprints and road networks has many useful applications including the monitoring of urban development, real-time navigation, etc. Taking into account that a great deal of human attention is ... -
A Deep Learning approach to land use classification in high resolution satellite imagery
(2020) Trabajo Fin de Máster/Master Amaierako LanaA lo largo de los últimos años ha aumentado el interés y la necesidad de disponer de información de usos y coberturas del territorio fiable y actualizada, siendo numerosos los proyectos de carácter local, nacional e ... -
Learning super-resolution for Sentinel-2 images with real ground truth data from a reference satellite
Galar Idoate, Mikel ; Sesma Redín, Rubén ; Ayala Lauroba, Christian; Albizua, Lourdes; Aranda, Carlos (Copernicus, 2020)
Contribución a congreso / Biltzarrerako ekarpena
Copernicus program via its Sentinel missions is making earth observation more accessible and affordable for everybody. Sentinel-2 images provide multi-spectral information every 5 days for each location. However, the maximum ... -
Multi-class strategies for joint building footprint and road detection in remote sensing
Ayala Lauroba, Christian; Aranda, Carlos; Galar Idoate, Mikel (MDPI, 2021)
Artículo / Artikulua
Building footprints and road networks are important inputs for a great deal of services. For instance, building maps are useful for urban planning, whereas road maps are essential for disaster response services. Traditionally, ... -
Multi-temporal data augmentation for high frequency satellite imagery: a case study in Sentinel-1 and Sentinel-2 building and road segmentation
Ayala Lauroba, Christian; Aranda Magallón, Coral; Galar Idoate, Mikel (ISPRS, 2022)
Contribución a congreso / Biltzarrerako ekarpena
Semantic segmentation of remote sensing images has many practical applications such as urban planning or disaster assessment. Deep learning-based approaches have shown their usefulness in automatically segmenting large ... -
Pushing the limits of Sentinel-2 for building footprint extraction
Ayala Lauroba, Christian; Aranda, Carlos; Galar Idoate, Mikel (IEEE, 2022)
Contribución a congreso / Biltzarrerako ekarpena
Building footprint maps are of high importance nowadays since a wide range of services relies on them to work. However, activities to keep these maps up-to-date are costly and time-consuming due to the great deal of human ... -
Super-resolution for Sentinel-2 images
Galar Idoate, Mikel ; Sesma Redín, Rubén ; Ayala Lauroba, Christian; Aranda, Carlos (International Society for Photogrammetry and Remote Sensing, 2019)
Contribución a congreso / Biltzarrerako ekarpena
Obtaining Sentinel-2 imagery of higher spatial resolution than the native bands while ensuring that output imagery preserves the original radiometry has become a key issue since the deployment of Sentinel-2 satellites. ... -
Super-resolution of Sentinel-2 images using convolutional neural networks and real ground truth data
Galar Idoate, Mikel ; Sesma Redín, Rubén ; Ayala Lauroba, Christian; Albizua, Lourdes; Aranda, Carlos (MDPI, 2020)
Artículo / Artikulua
Earth observation data is becoming more accessible and affordable thanks to the Copernicus programme and its Sentinel missions. Every location worldwide can be freely monitored approximately every 5 days using the ... -
Towards fine-grained road maps extraction using sentinel-2 imagery
Ayala Lauroba, Christian; Aranda, Carlos; Galar Idoate, Mikel (Copernicus, 2021)
Contribución a congreso / Biltzarrerako ekarpena
Nowadays, it is highly important to keep road maps up-to-date since a great deal of services rely on them. However, to date, these labours have demanded a great deal of human attention due to their complexity. In the last ...
