diff --git a/docs/make.jl b/docs/make.jl
index 88229b48..913af4b9 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -28,6 +28,8 @@ examples = [
     "orthographic.jl",
     "german_lakes.jl",
     "geostationary_image.jl",
+    "multiple_crs.jl",
+    joinpath("gmt", "antioquia.jl"),
     "contourf.jl",
     "world_population.jl",
     "field_and_countries.jl",
@@ -35,7 +37,8 @@ examples = [
     "projections.jl",
     "tissot.jl",
     "rotating_earth.jl",
-    joinpath("gmt", "antioquia.jl"),
+    "rasters.jl",
+    
 ]
 example_dir = joinpath(dirname(@__DIR__), "examples")
 for filename in examples
diff --git a/examples/rasters.jl b/examples/rasters.jl
index bfd672be..05e52c28 100644
--- a/examples/rasters.jl
+++ b/examples/rasters.jl
@@ -1,7 +1,40 @@
+# # Raster data (with Rasters.jl)
+# [Rasters.jl](https://github.com/rafaqz/Rasters.jl) is a Julia package designed for working with raster data. 
+# It provides tools to read, write, and manipulate raster datasets, 
+# which are commonly used in geographic information systems (GIS), 
+# remote sensing, and similar fields where grid data is prevalent.
+# It's built on top of [DimensionalData.jl](https://github.com/rafaqz/DimensionalData.jl), which also underpins e.g. [YAXArrays.jl](https://github.com/JuliaDataCubes/YAXArrays.jl).
+#
+# In general, any input that works with base Makie will work with GeoMakie in a GeoAxis!
+
+# First, we'll load Rasters.jl, RasterDataSources.jl which provides access to common datasets, and ArchGDAL.jl which Rasters.jl depends on to read files.
 using Rasters, RasterDataSources, ArchGDAL
+# We'll also load GeoMakie and CairoMakie to plot the data.
 using GeoMakie, CairoMakie
 
-#
+# First, we can load a Raster from the [EarthEnv](earthenv.org) project, which represents habitat or ecosystem heterogeneity.
+
+ras = Raster(EarthEnv{HabitatHeterogeneity}, :homogeneity) # habitat homogeneity to neighbouring pixel
+# Let's take a look at this in regular Makie first:
+heatmap(ras; axis = (; aspect = DataAspect()))
+# We can plot this in any projection:
+fig = Figure(); ga = GeoAxis(fig[1, 1])
+hm = heatmap!(ga, ras)
+fig
+# We can also change the projection arbitrarily:
+ga.dest[] = "+proj=ortho +lon_0=19 +lat_0=72"
+fig
+# and all other Makie keyword arguments also apply!
+hm.colormap = :isoluminant_cgo_70_c39_n256
+fig
+# You can also use other recipes like surface:
+fig = Figure(); ga = GeoAxis(fig[1, 1])
+sp = surface!(ga, ras)
+fig
+# This looks a bit strange - but you can always disable shading:
+sp.shading = NoShading
+fig
+# See also the Geostationary image and Multiple CRS examples, where we explore how to plot data in different projections.
 # make cover image #jl
 mkpath("covers") #hide
 save("covers/$(splitext(basename(@__FILE__))[1]).png", fig) #hide
diff --git a/examples/source_crs.jl b/examples/source_crs.jl
index de101bf8..ab284084 100644
--- a/examples/source_crs.jl
+++ b/examples/source_crs.jl
@@ -18,10 +18,6 @@ webmerc_geojson = GO.reproject(GI.convert.((GI,), geojson) |> GO.tuples; transfo
 
 london = Rect2d(-0.0921, 51.5, 0.04, 0.025)
 
-function Makie.MakieCore.attribute_name_allowlist()
-    (:xautolimits, :yautolimits, :zautolimits, :label, :rasterize, :model, :transformation, :dest, :source, :specular, :matcap, :backlight, :shininess, :interpolate, :diffuse,:dim_conversions)
-end
-
 fig = Figure()
 nax = Axis(fig[1, 1]; aspect = DataAspect())
 m = Tyler.Map(london, figure = fig, axis = nax)