Neural networks for semantic segmentation of historical city maps: Cross-cultural performance and the impact of figurative diversity

Petitpierre, Rémi

doi:10.13140/rg.2.2.10973.64484

Cited by 2 publications

(1 citation statement)

References 69 publications

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“…The Nolli map was automatically vectorized by means of semantic segmentation. A Convolutional Neural Network was trained on historical map patches to detect which pixels represented buildings [18]. Following the inference of the trained model on the Nolli map, the building-labeled pixel regions were converted to vector polygons.…”

Section: The Map As a Guidementioning

confidence: 99%

Transhistorical Urban Landscape as Hypermap

del Castillo,

Neri,

Guhennec

et al. 2023

Proceedings of the 34th ACM Conference on Hypertext and Social Media

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This article explores the conception, design, and implementation of a hypertextual map that we call hypermap. Using Giovanni Nolli's 1748 map of the city of Rome as a backbone, we conducted an experiment based on one of the routes defined by Giuseppe Vasi's Grand Tour of Rome to collect various types of urban and environmental information, thus aiming to connect a multiplicity of data from different nature and times periods to enhance the serendipitous elaboration of new narratives, interpretations, and data (namely "unfolding") not implicitly enacted by the pure analytical and mechanistic overlapping of gathered data ("folding"). This experiment is part of the research project entitled Datathink that was conducted at the Bibliotheca Hertziana -Max Planck Institute for Art History in Rome, the experiment serves as a proof of concept for an augmented database of the urban landscape in the city of Rome and new ways to facilitate the access and enhancement of cultural artifacts and knowledge.

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Section: The Map As a Guidementioning

confidence: 99%

Transhistorical Urban Landscape as Hypermap

del Castillo,

Neri,

Guhennec

et al. 2023

Proceedings of the 34th ACM Conference on Hypertext and Social Media

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Developing Versatile Graphic Map Load Metrics

Barvíř

Voženílek

2020

IJGI

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Graphic map load is a property of a map quantifying the amount of map content. It indicates the visual complexity of the map and helps cartographers to adapt maps and other geospatial visualizations to accomplish their purpose. Generally, map design needs to enable the user to quickly, comprehensively, and intuitively obtain the relevant spatial information from a map. Especially, this applies in cases like crisis management, immunology and military. However, there are no widely applicable metrics to assess the complexity of cartographic products. This paper evaluates seven simple metrics for graphic map load calculation based on image analytics using the set of 50 various maps on an easily understandable scale of 0–100%. The metrics are compared to values of user-perceived map load survey joined by 62 respondents. All the suggested metrics are designed for calculation with easy-accessible software and therefore suitable for use in any user environment. Metrics utilizing the principle of edge detection have been found suitable for a diversity of geospatial visualizations providing the best results among other metrics.

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Neural networks for semantic segmentation of historical city maps: Cross-cultural performance and the impact of figurative diversity

Cited by 2 publications

References 69 publications

Transhistorical Urban Landscape as Hypermap

Transhistorical Urban Landscape as Hypermap

Developing Versatile Graphic Map Load Metrics

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