A Reproducible Workflow for Scraping, Structuring, and Segmenting Legacy Archaeological Artifact Images
This addresses the challenge of making web-based archaeological image collections machine learning-friendly for researchers in digital archaeology, though it is incremental as it applies existing tools to a new domain.
The paper tackles the problem of converting a legacy archaeological image collection into a structured dataset by developing a reproducible workflow, resulting in a lightweight approach that generates binary masks, bounding boxes, and annotations for thousands of images.
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.