Generative Text-to-Image Diffusion for Automated Map Production Based on Geosocial Media Data

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Abstract

The state of generative AI has taken a leap forward with the availability of open source diffusion models. Here, we demonstrate an integrated workflow that uses text-to-image stable diffusion at its core to automatically generate icon maps such as for the area of the Großer Garten, a tourist hotspot in Dresden, Germany. The workflow is based on the aggregation of geosocial media data from Twitter, Flickr, Instagram and iNaturalist. This data are used to create diffusion prompts to account for the collective attribution of meaning and importance by the population in map generation. Specifically, we contribute methods for simplifying the variety of contexts communicated on social media through spatial clustering and semantic filtering for use in prompts, and then demonstrate how this human-contributed baseline data can be used in prompt engineering to automatically generate icon maps. Replacing labels on maps with expressive graphics has the general advantage of reaching a broader audience, such as children and other illiterate groups. For example, the resulting maps can be used to inform tourists of all backgrounds about important activities, points of interest, and landmarks without the need for translation. Several challenges are identified and possible future optimizations are described for different steps of the process. The code and data are fully provided and shared in several Jupyter notebooks, allowing for transparent replication of the workflow and adoption to other domains or datasets.

Titel in Übersetzung
Automatische Kartenproduktion mithilfe generativer Text-zu-Bild Diffusion unter Nutzung von raumbezogenen Daten sozialer Medien

Details

OriginalspracheEnglisch
Seiten (von - bis)3-15
Seitenumfang13
FachzeitschriftKN - Journal of Cartography and Geographic Information
Jahrgang74
Ausgabenummer1
PublikationsstatusVeröffentlicht - 13 Feb. 2024
Peer-Review-StatusJa

Externe IDs

ORCID /0000-0003-2949-4887/work/153652869
Scopus 85184877372
Mendeley d83b7c3f-bca4-30ab-a932-1316c12e2b7b

Schlagworte

Schlagwörter

  • Cartography, Diffusion models, Generative AI, Image-to-image, Social media, Text-to-image