# Transfer Learning of Semantic Segmentation Methods for Identifying Buried Archaeological Structures on LiDAR Data

Source: [Transfer Learning of Semantic Segmentation Methods for Identifying Buried Archaeological Structures on LiDAR Data](<https://www.sota2.com/research/paper/68fa70e71e209131cea4dd26>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:52:52.049Z

Authors: Gregory Sech, Paolo Soleni, Wouter B. Verschoof-van der Vaart, \\v{Z}iga Kokalj, Arianna Traviglia, Marco Fiorucci

Published: 2023-07-07T11:00:44.000Z

## Abstract

When applying deep learning to remote sensing data in archaeological research, a notable obstacle is the limited availability of suitable datasets for training models. The application of transfer learning is frequently employed to mitigate this drawback. However, there is still a need to explore its effectiveness when applied across different archaeological datasets. This paper compares the performance of various transfer learning configurations using two semantic segmentation deep neural networks on two LiDAR datasets. The experimental results indicate that transfer learning-based approaches in archaeology can lead to performance improvements, although a systematic enhancement has not yet been observed. We provide specific insights about the validity of such techniques that can serve as a baseline for future works.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2307.03512>)

[PDF](<https://arxiv.org/pdf/2307.03512.pdf>)