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

• URL: http://arxiv.org/abs/2512.11817v1
• Date: Thu, 27 Nov 2025 14:29:05 GMT
• Title: A Reproducible Workflow for Scraping, Structuring, and Segmenting Legacy Archaeological Artifact Images
• Authors: Juan Palomeque-Gonzalez,
• Abstract summary: The case study focuses on the Lower Palaeolithic hand axe and biface collection curated by the Archaeology Data Service (ADS)<n>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.<n>The original images are not redistributed, and only derived products such as masks, outlines, and annotations are shared.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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