Image

data media type

• Interactive map of Building Designs in Tartu

  This workflow was created during the HUM Hackathon and focuses on building an interactive map application based on the Tartu Building Designs database. The main purpose of this project was to visualise building projects in Tartu between 1870 and 1920. The workflow describes data processing and creating a map application.

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• Spatial and Temporal Patterns of the Photograph Collection of the EFA

  The aim of this workflow is to study the spatial and temporal patterns of the photograph collection of the Estonian Folklore Archives (EFA) and to make a large and difficult-to-grasp dataset easier to explore through an interactive visualisation tool. The project focuses on the first 10,000 black-and-white photographs from the EFA photograph collection, which contains approximately 88,000 photographs in total, together with their metadata. On the basis of this material, the distribution of photographs is analysed across time, space, keywords, collection projects, persons, and photographers.

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• Use cases for working photos of material culture researchers

  This project investigates how working photographs collected by material culture researchers can be used to automatically identify objects and assess their condition, and how such a large collection can be published under FAIR principles in a user-friendly way. Researchers of material culture may take thousands of photos of objects within the scope of each research topic, but in most cases, the use of photos is limited to typological identification, visual comparison of finds, and...

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• Pattern Analysis of Striped Skirts

  This workflow describes how image-based patterns are compared using machine learning methods. The aim is to investigate the pattern logic that characterised Estonian striped skirts of the 18th–19th centuries and to examine how these patterns relate to striped, glazed-surface woollen callimanco fabrics produced at the Norwich factory in England. The workflow combines digital humanities approaches with machine learning. For analysing large image datasets, we use machine learning models designed for visual pattern recognition, specifically convolutional neural networks (CNNs).

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