no code implementations • 8 Apr 2024 • Raveerat Jaturapitpornchai, Giulio Poggi, Gregory Sech, Ziga Kokalj, Marco Fiorucci, Arianna Traviglia
Deep learning methods in LiDAR-based archaeological research often leverage visualisation techniques derived from Digital Elevation Models to enhance characteristics of archaeological objects present in the images.
no code implementations • 8 Apr 2024 • Gregory Sech, Giulio Poggi, Marina Ljubenovic, Marco Fiorucci, Arianna Traviglia
Hyperspectral data recorded from satellite platforms are often ill-suited for geo-archaeological prospection due to low spatial resolution.
1 code implementation • 28 Jul 2023 • Peter Naylor, Diego Di Carlo, Arianna Traviglia, Makoto Yamada, Marco Fiorucci
We outperform the previous methods by a margin of 10% in the intersection over union metric.
no code implementations • 7 Jul 2023 • Gregory Sech, Paolo Soleni, Wouter B. Verschoof-van der Vaart, Žiga Kokalj, Arianna Traviglia, Marco Fiorucci
When applying deep learning to remote sensing data in archaeological research, a notable obstacle is the limited availability of suitable datasets for training models.
1 code implementation • 14 Feb 2023 • Marco Fiorucci, Peter Naylor, Makoto Yamada
The method is based on unbalanced optimal transport and can be generalised to any change detection problem with LiDAR data.
no code implementations • 16 Sep 2019 • Marco Fiorucci
This study provide us a principled way to develop a graph decomposition algorithm based on stochastic block model which is fitted using likelihood maximization.
no code implementations • 21 Mar 2017 • Marco Fiorucci, Alessandro Torcinovich, Manuel Curado, Francisco Escolano, Marcello Pelillo
In this paper we analyze the practical implications of Szemer\'edi's regularity lemma in the preservation of metric information contained in large graphs.
no code implementations • 21 Sep 2016 • Marcello Pelillo, Ismail Elezi, Marco Fiorucci
Introduced in the mid-1970's as an intermediate step in proving a long-standing conjecture on arithmetic progressions, Szemer\'edi's regularity lemma has emerged over time as a fundamental tool in different branches of graph theory, combinatorics and theoretical computer science.