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Found 7 papers, 1 papers with code
Impact of LiDAR visualisations on semantic segmentation of archaeological objects
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.
Pansharpening of PRISMA products for archaeological prospection
Hyperspectral data recorded from satellite platforms are often ill-suited for geo-archaeological prospection due to low spatial resolution.
Super-resolution of THz time-domain images based on low-rank representation
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.
Implicit neural representation for change detection
We outperform the previous methods by a margin of 10% in the intersection over union metric.
Transfer Learning of Semantic Segmentation Methods for Identifying Buried Archaeological Structures on LiDAR Data
When applying deep learning to remote sensing data in archaeological research, a notable obstacle is the limited availability of suitable datasets for training models.
Relaxation Labeling Meets GANs: Solving Jigsaw Puzzles with Missing Borders
This paper proposes JiGAN, a GAN-based method for solving Jigsaw puzzles with eroded or missing borders.
Beam-Shape Effects and Noise Removal from THz Time-Domain Images in Reflection Geometry in the 0.25-6 THz Range
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.
