Catalog layers

You don't have to build every layer from your own data. The Add a catalog layer panel lets you drop in ready-made, curated geospatial layers.

There are two kinds of catalog:

  • The Icon Map Catalog — a public library of thousands of curated layers hosted by Tekantis: boundaries, demographics, infrastructure, and reference data.
  • Your Organizational Catalog — layers your own organization has curated and stored in OneLake, which appear under a separate tab.

A catalog layer is a reference layer — it brings its own geometry. You can use it purely for context, or bind your data to it to make it data-driven (see Binding your data to a catalog layer below), so a single catalog layer works both ways.

Finding a layer

Search the catalog by keyword, filter by tags and regions, or use AI search to describe what you're looking for in plain language. Layers are grouped by geometry type (point, line, polygon) and, where relevant, offered as switchable, multi-resolution groups with several variants.

Each layer has an About panel (source, license, tags, and often a video and dataset page) and a preview map so you can see it before adding it.

Styling a catalog layer

Catalog layers come with a full style step — fill and gradient rules, outline, 3D extrusion, points, icons, labels, filters, and a zoom range — so a hosted boundary or reference layer matches the look of the rest of your map.

Binding your data to a catalog layer

Catalog boundary layers can act as the geometry for your data: bind your data source to the layer (for example, sales by postcode to a postcode-boundary layer) to make a data-driven choropleth without holding the geometry yourself. Once bound, the layer is styled, filtered, selectable, and cross-filters your charts just like a data-bound layer — the only difference is that its shapes come from the catalog rather than from your data source.

Adding reference data to OneLake

Behind each tile layer is a bounds dataset (Parquet) with each feature's attributes. You can preview this reference data and use Add to Lakehouse to bring it into your OneLake for use elsewhere in Fabric.

Next steps