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.
no code implementations • 21 Dec 2023 • Marina Ljubenovic, Alessia Artesani, Stefano Bonetti, Arianna Traviglia
Terahertz time-domain spectroscopy (THz-TDS) employs sub-picosecond pulses to probe dielectric properties of materials giving as a result a 3-dimensional hyperspectral data cube.
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.
no code implementations • 28 Mar 2022 • Marina Khoroshiltseva, Arianna Traviglia, Marcello Pelillo, Sebastiano Vascon
This paper proposes JiGAN, a GAN-based method for solving Jigsaw puzzles with eroded or missing borders.
no code implementations • 1 Mar 2022 • Marina Ljubenovic, Alessia Artesani, Stefano Bonetti, Arianna Traviglia
This mode is often the only one that can be effectively used in most cases, for example when analyzing objects that are either opaque in the THz range, or that cannot be displaced from their location (e. g., museums), such as those of cultural interest.