diff --git a/src/array.jl b/src/array.jl
index 42c2eaab..4e75fb72 100644
--- a/src/array.jl
+++ b/src/array.jl
@@ -239,9 +239,6 @@ struct Raster{T,N,D<:Tuple,R<:Tuple,A<:AbstractArray{T,N},Na,Me,Mi<:Union{T,Noth
         new{T,N,D,R,A,Na,Me,typeof(missingval1)}(data, dims, refdims, name, metadata, missingval1)
     end
 end
-Raster(f::Function, args...; kw...) = Raster(args...; f, kw...)
-# For ambiguity with dataset methods
-Raster(f::Function, s::AbstractString; kw...) = Raster(s; f, kw...)
 # Create a Raster from and AbstractArray and dims
 function Raster(A::AbstractArray{T,N}, dims::Tuple;
     refdims=(),
diff --git a/src/utils.jl b/src/utils.jl
index 6cafda87..69ccd0fb 100644
--- a/src/utils.jl
+++ b/src/utils.jl
@@ -97,7 +97,7 @@ _type_missingval(::Type{T}) where T = _type_missingval1(Missings.nonmissingtype(
 
 _type_missingval1(::Type{T}) where T<:Number = typemin(T)
 _type_missingval1(::Type{T}) where T<:Unsigned = typemax(T)
-_type_missingval1(::Type{<:AbstractString}) where T = T("")
+_type_missingval1(::Type{T}) where T<:AbstractString = T("")
 
 _fix_missingval(::Type, ::Union{NoKW,Nothing}) = nothing
 _fix_missingval(::AbstractArray, ::Nothing) = nothing
diff --git a/test/warp.jl b/test/warp.jl
index b23c13a6..7e8f2e73 100644
--- a/test/warp.jl
+++ b/test/warp.jl
@@ -14,7 +14,6 @@ gdalpath = maybedownload(url)
     @test size(warped) == (720, 721)
     # the crs is rotated so the image is rotated an all four corners should be missing
     missingval(warped) === 0xff
-    parent(warped)
     @test warped[1, 1] === warped[1, end] === warped[end, 1] === warped[end, end] === 0xff == missingval(warped)
     # now compute mean squared error of the back transformation
     res = map(step, lookup(r))