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20212026
most citedRadio astronomical images object detection and segmentation: A benchmark on deep learning methods

18 citations · 28 across the 7 of their papers we have counts for

collaborators

7 papers

astro-ph.IM2026

Transformer-Based Source Detection and Morphological Classification in LOFAR Deep-Field Continuum Images

Guangwen Chen, Kristian Z. Adami, John Abela +7

Radio source detection and morphological classification are fundamental for exploiting the scientific potential of modern radio continuum surveys. However, the rapidly increasing d…

cs.CV2023★ 1 cited

RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation

Renato Sortino, Thomas Cecconello, Andrea DeMarco +10

Along with the nearing completion of the Square Kilometre Array (SKA), comes an increasing demand for accurate and reliable automated solutions to extract valuable information from…

cs.CV2023★ 18 cited

Radio astronomical images object detection and segmentation: A benchmark on deep learning methods

Renato Sortino, Daniel Magro, Giuseppe Fiameni +9

In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being e…

astro-ph.IM2023

Radio source analysis services for the SKA and precursors

Simone Riggi, Cristobal Bordiu, Daniel Magro +8

New developments in data processing and visualization are being made in preparation for upcoming radioastronomical surveys planned with the Square Kilometre Array (SKA) and its pre…

astro-ph.IM2022★ 1 cited

Astronomical source detection in radio continuum maps with deep neural networks

S. Riggi, D. Magro, R. Sortino +13

Source finding is one of the most challenging tasks in upcoming radio continuum surveys with SKA precursors, such as the Evolutionary Map of the Universe (EMU) survey of the Austra…

astro-ph.IM2021

Astronomical source finding services for the CIRASA visual analytic platform

S. Riggi, C. Bordiu, F. Vitello +11

Innovative developments in data processing, archiving, analysis, and visualization are nowadays unavoidable to deal with the data deluge expected in next-generation facilities for…

astro-ph.IM2021★ 8 cited

A Comparative Study of Convolutional Neural Networks for the Detection of Strong Gravitational Lensing

Daniel Magro, Kristian Zarb Adami, Andrea DeMarco +2

As we enter the era of large-scale imaging surveys with the up-coming telescopes such as LSST and SKA, it is envisaged that the number of known strong gravitational lensing systems…