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Found 8 papers, 2 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.
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.
Optimal Transport for Change Detection on LiDAR Point Clouds
The method is based on unbalanced optimal transport and can be generalised to any change detection problem with LiDAR data.
Regular Partitions and Their Use in Structural Pattern Recognition
This study provide us a principled way to develop a graph decomposition algorithm based on stochastic block model which is fitted using likelihood maximization.
On the Interplay between Strong Regularity and Graph Densification
In this paper we analyze the practical implications of Szemer\'edi's regularity lemma in the preservation of metric information contained in large graphs.
Revealing Structure in Large Graphs: Szemerédi's Regularity Lemma and its Use in Pattern Recognition
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.
