Zobrazeno 1 - 10
of 425
pro vyhledávání: '"A. Marsocci"'
Autor:
Marsocci, Valerio, Audebert, Nicolas
Large-scale "foundation models" have gained traction as a way to leverage the vast amounts of unlabeled remote sensing data collected every day. However, due to the multiplicity of Earth Observation satellites, these models should learn "sensor agnos
Externí odkaz:
http://arxiv.org/abs/2405.09922
Autor:
Scardapane, Simone, Baiocchi, Alessandro, Devoto, Alessio, Marsocci, Valerio, Minervini, Pasquale, Pomponi, Jary
Publikováno v:
Intelligenza Artificiale, vol. Pre-press, pp. 1-16, 2024
This article summarizes principles and ideas from the emerging area of applying \textit{conditional computation} methods to the design of neural networks. In particular, we focus on neural networks that can dynamically activate or de-activate parts o
Externí odkaz:
http://arxiv.org/abs/2403.07965
Land cover maps are a pivotal element in a wide range of Earth Observation (EO) applications. However, annotating large datasets to develop supervised systems for remote sensing (RS) semantic segmentation is costly and time-consuming. Unsupervised Do
Externí odkaz:
http://arxiv.org/abs/2304.07750
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing 196 (2023) 325-339
Change detection is one of the most active research areas in Remote Sensing (RS). Most of the recently developed change detection methods are based on deep learning (DL) algorithms. This kind of algorithms is generally focused on generating two-dimen
Externí odkaz:
http://arxiv.org/abs/2205.15903
Autor:
Marsocci, Valerio, Scardapane, Simone
In the field of Earth Observation (EO), Continual Learning (CL) algorithms have been proposed to deal with large datasets by decomposing them into several subsets and processing them incrementally. The majority of these algorithms assume that data is
Externí odkaz:
http://arxiv.org/abs/2205.11319
Autor:
Valerio Marsocci, Simone Scardapane
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 5049-5060 (2023)
In the field of earth observation (EO), continual learning (CL) algorithms have been proposed to deal with large datasets by decomposing them into several subsets and processing them incrementally. The majority of these algorithms assume that data ar
Externí odkaz:
https://doaj.org/article/0f6bb5b21e3a4f098ae5dccf5d3d3db7
Autor:
Dozio, Nicoló, Marcolin, Federica, Scurati, Giulia Wally, Ulrich, Luca, Nonis, Francesca, Vezzetti, Enrico, Marsocci, Gabriele, La Rosa, Alba, Ferrise, Francesco
Publikováno v:
In International Journal of Human - Computer Studies June 2022 162
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B3-2022, Pp 1349-1354 (2022)
Change detection is one of the main topics in Earth Observation, due to its wide range of applications, varying from urban development monitoring to natural disaster management. Most of the recently developed change detection methodologies rely on th
Externí odkaz:
https://doaj.org/article/3aabcfb876a04d2e83efcb4bb7c73982
Autor:
Pannuti, Lorenzo1, Marsocci, Antonio1, Curti, Francesca1, Maggi, Giuseppe1, Petrone, Brunella1, Peretti, Ambrogio1, Ruberti, Enzo1, Magnifica, Fabrizio2,3 fabrizio.magnifica@uniroma1.it
Publikováno v:
Muscles, Ligaments & Tendons Journal (MLTJ). Jul-Sep2023, Vol. 13 Issue 3, p383-393. 11p.
Autor:
Grassi, Angelica1, Marsocci, Antonio1, Dell'Anno, Federico1, Castiglia, Stefano Filippo2, Fattori, Simona3, Magnifica, Fabrizio4,5 fabrizio.magnifica@uniroma1.it
Publikováno v:
Muscles, Ligaments & Tendons Journal (MLTJ). Jan-Mar2023, Vol. 13 Issue 1, p119-125. 7p.