A Reproducible Workflow for Scraping, Structuring, and Segmenting Legacy Archaeological Artifact Images

TL;DR

This paper introduces a reproducible workflow combining web scraping and classical computer vision techniques to convert legacy archaeological images into structured, segmentation-ready datasets, enhancing digital archaeology research.

Contribution

It presents open source tools for automated image retrieval and processing, enabling structured datasets from legacy collections without redistributing original images.

Findings

Successfully retrieved and processed thousands of archaeological images

Generated segmentation masks and annotations compatible with machine learning workflows

Facilitated reproducible and ethical data handling in digital archaeology

Abstract

This technical note presents a reproducible workflow for converting a legacy archaeological image collection into a structured and segmentation ready dataset. The case study focuses on the Lower Palaeolithic hand axe and biface collection curated by the Archaeology Data Service (ADS), a dataset that provides thousands of standardised photographs but no mechanism for bulk download or automated processing. To address this, two open source tools were developed: a web scraping script that retrieves all record pages, extracts associated metadata, and downloads the available images while respecting ADS Terms of Use and ethical scraping guidelines; and an image processing pipeline that renames files using UUIDs, generates binary masks and bounding boxes through classical computer vision, and stores all derived information in a COCO compatible Json file enriched with archaeological metadata. The original images are not redistributed, and only derived products such as masks, outlines, and annotations are shared. Together, these components provide a lightweight and reusable approach for transforming web based archaeological image collections into machine learning friendly formats, facilitating downstream analysis and contributing to more reproducible research practices in digital archaeology.