no code implementations • 17 Feb 2021 • Claudio Labanti, Lorenzo Amati, Filippo Frontera, Sandro Mereghetti, José Luis Gasent-Blesa, Christoph Tenzer, Piotr Orleanski, Irfan Kuvvetli, Riccardo Campana, Fabio Fuschino, Luca Terenzi, Enrico Virgilli, Gianluca Morgante, Mauro Orlandini, Reginald C. Butler, John B. Stephen, Natalia Auricchio, Adriano De Rosa, Vanni Da Ronco, Federico Evangelisti, Michele Melchiorri, Stefano Squerzanti, Mauro Fiorini, Giuseppe Bertuccio, Filippo Mele, Massimo Gandola, Piero Malcovati, Marco Grassi, Pierluigi Bellutti, Giacomo Borghi, Francesco Ficorella, Antonino Picciotto, Vittorio Zanini, Nicola Zorzi, Evgeny Demenev, Irina Rashevskaya, Alexander Rachevski, Gianluigi Zampa, Andrea Vacchi, Nicola Zampa, Giuseppe Baldazzi, Giovanni La Rosa, Giuseppe Sottile, Angela Volpe, Marek Winkler, Victor Reglero, Paul H. Connell, Benjamin Pinazo-Herrero, Javier Navarro-González, Pedro Rodríguez-Martínez, Alberto J. Castro-Tirado, Andrea Santangelo, Paul Hedderman, Paolo Lorenzi, Paolo Sarra, Søren M. Pedersen, Denis Tcherniak, Cristiano Guidorzi, Piero Rosati, Alessio Trois, Raffaele Piazzolla
THESEUS is one of the three missions selected by ESA as fifth medium class mission (M5) candidates in its Cosmic Vision science program, currently under assessment in a phase A study with a planned launch date in 2032.
Instrumentation and Methods for Astrophysics
no code implementations • 17 Feb 2021 • José Luis Gasent-Blesa, Víctor Reglero, Paul Connell, Benjamín Pinazo-Herrero, Javier Navarro-González, Pedro Rodríguez-Martínez, Alberto J. Castro-Tirado, María Dolores Caballero-García, Lorenzo Amati, Claudio Labanti, Sandro Mereghetti, Filippo Frontera, Riccardo Campana, Mauro Orlandini, John Stephen, Luca Terenzi, Federico Evangelisti, Stefano Squerzanti, Michele Melchiorri, Fabio Fuschino, Adriano De Rosa, Gianluca Morgante
This contribution is implemented by the XGIS Imaging System, based on that technique.
Instrumentation and Methods for Astrophysics
1 code implementation • 5 Jun 2020 • Christina Corbane, Vasileios Syrris, Filip Sabo, Panagiotis Politis, Michele Melchiorri, Martino Pesaresi, Pierre Soille, Thomas Kemper
Spatially consistent and up-to-date maps of human settlements are crucial for addressing policies related to urbanization and sustainability, especially in the era of an increasingly urbanized world. The availability of open and free Sentinel-2 data of the Copernicus Earth Observation program offers a new opportunity for wall-to-wall mapping of human settlements at a global scale. This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 m from a global composite of Sentinel-2 imagery. A multi-neuro modeling methodology building on a simple Convolution Neural Networks architecture for pixel-wise image classification of built-up areas is developed. The core features of the proposed model are the image patch of size 5 x 5 pixels adequate for describing built-up areas from Sentinel-2 imagery and the lightweight topology with a total number of 1, 448, 578 trainable parameters and 4 2D convolutional layers and 2 flattened layers. The deployment of the model on the global Sentinel-2 image composite provides the most detailed and complete map reporting about built-up areas for reference year 2018.