mlsanityData to 13 Oct 2026

A roadmap for generative mapping: unlocking the power of generative AI for map-making

arXiv 2410.15770 ↗First posted 21 Oct 2024cs.AIcited by 2

Not on the map. Why?

Abstract (arXiv)

Maps are broadly relevant across various fields, serving as valuable tools for presenting spatial phenomena and communicating spatial knowledge. However, map-making is still largely confined to those with expertise in GIS and cartography due to the specialized software and complex workflow involved, from data processing to visualization. While generative AI has recently demonstrated its remarkable capability in creating various types of content and its wide accessibility to the general public, its potential in generating maps is yet to be fully realized. This paper highlights the key applications of generative AI in map-making, summarizes recent advancements in generative AI, identifies the specific technologies required and the challenges of using current methods, and provides a roadmap for developing a generative mapping system (GMS) to make map-making more accessible.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 10 of its 30 references that are in the map. First posted on arXiv 21 Oct 2024.

Builds on

10 of 30 references.

References · 10

  1. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,028
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,213
  3. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,604
  4. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,240
  5. 2024Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Predictioncited by 1,012
  6. 2019An Introduction to Variational Autoencoderscited by 998
  7. 2022Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priorscited by 487
  8. 2022SpaText: Spatio-Textual Representation for Controllable Image Generationcited by 217
  9. 2020DeepSVG: A Hierarchical Generative Network for Vector Graphics Animationcited by 158
  10. 2021Diff-TTS: A Denoising Diffusion Model for Text-to-Speechcited by 156

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Citing papers per year: 2025: 2.

2 papers.