# Visual AI for geospatial data

> Visual AI for geospatial data - Learn how to use Visual AI to represent geospatial data for enhanced
> analysis.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:09.586857+00:00` (UTC).

## Primary page

- [Visual AI for geospatial data](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/viz-geo.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [Time series and specific use cases](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/index.html): Linked from this page.

## Documentation content

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/VisualAI_for_geospatial/Visual%20AI%20for%20geospatial%20data.ipynb)

This accelerator shows how you can use Visual AI on geospatial data. Instead of deriving numeric features from the georeferenced data, you look at the geospatial data as images. For example, if you have a map of population distribution, instead of extracting the population that corresponds to each row of the main table you can pass the region of the map that corresponds to that row. This provides more information than a raw count of the population would, as it also encodes the distribution within the region (is it uniform or does it concentrate in some areas? what is the shape?, etc.)

The example used to illustrate the approach comes from work done with the Virtue Foundation. As part of the "Data Mapping Initiative", DataRobot has built models to identify suitable locations for new healthcare facilities. By looking at the location of existing hospitals and clinics as a function of several features (road networks, population, terrain, etc.) you find which other areas are suitable in terms of these features (similar to a propensity model).