diff --git a/pyproject.toml b/pyproject.toml
index f8357df5..58accf0e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "ssb-sgis"
-version = "1.0.7"
+version = "1.0.8"
 description = "GIS functions used at Statistics Norway."
 authors = ["Morten Letnes <morten.letnes@ssb.no>"]
 license = "MIT"
diff --git a/src/sgis/helpers.py b/src/sgis/helpers.py
index 136b5875..2067361d 100644
--- a/src/sgis/helpers.py
+++ b/src/sgis/helpers.py
@@ -72,19 +72,29 @@ def to_numpy_func(text: str) -> Callable:
     raise ValueError
 
 
-def is_property(obj: object, attribute: str) -> bool:
+def is_property(obj: object, attr: str) -> bool:
     """Determine if a class attribute is a property.
 
     Args:
         obj: The object to check.
-        attribute: The attribute name to check on the object.
+        attr: The attribute name to check on the object.
 
     Returns:
         True if the attribute is a property, False otherwise.
     """
-    return hasattr(obj.__class__, attribute) and isinstance(
-        getattr(obj.__class__, attribute), property
-    )
+    if not hasattr(obj.__class__, attr):
+        return False
+    if isinstance(obj, type):
+        return isinstance(getattr(obj, attr), property)
+    else:
+        return isinstance(getattr(obj.__class__, attr), property)
+
+
+def is_method(obj: Any, attr: str) -> bool:
+    if isinstance(obj, type):
+        return inspect.ismethod(getattr(obj, attr, None))
+    else:
+        return inspect.ismethod(getattr(obj.__class__, attr, None))
 
 
 def dict_zip_intersection(*dicts: dict) -> Generator[tuple[Any, ...], None, None]:
diff --git a/src/sgis/raster/image_collection.py b/src/sgis/raster/image_collection.py
index a681bf06..fc36a738 100644
--- a/src/sgis/raster/image_collection.py
+++ b/src/sgis/raster/image_collection.py
@@ -95,6 +95,8 @@ class Dataset:
 from ..helpers import _fix_path
 from ..helpers import get_all_files
 from ..helpers import get_numpy_func
+from ..helpers import is_method
+from ..helpers import is_property
 from ..io._is_dapla import is_dapla
 from ..io.opener import opener
 from . import sentinel_config as config
@@ -164,6 +166,7 @@ def _read_parquet_func(*args, **kwargs) -> list[str]:
     "backend",
     "masking",
     "_merged",
+    "date",
 ]
 
 _load_counter: int = 0
@@ -319,7 +322,7 @@ def __init__(self, *, metadata=None, bbox=None, **kwargs) -> None:
         self._bounds = None
         self._merged = False
         self._from_array = False
-        self._from_gdf = False
+        self._from_geopandas = False
         self.metadata_attributes = self.metadata_attributes or {}
         self._path = None
         self._metadata_from_xml = False
@@ -328,32 +331,30 @@ def __init__(self, *, metadata=None, bbox=None, **kwargs) -> None:
 
         self.metadata = self._metadata_to_nested_dict(metadata)
 
-        if self.filename_regexes:
-            if isinstance(self.filename_regexes, str):
-                self.filename_regexes = (self.filename_regexes,)
-            self.filename_patterns = [
-                re.compile(regexes, flags=re.VERBOSE)
-                for regexes in self.filename_regexes
-            ]
-        else:
-            self.filename_patterns = ()
-
-        if self.image_regexes:
-            if isinstance(self.image_regexes, str):
-                self.image_regexes = (self.image_regexes,)
-            self.image_patterns = [
-                re.compile(regexes, flags=re.VERBOSE) for regexes in self.image_regexes
-            ]
-        else:
-            self.image_patterns = ()
+        self.image_patterns = self._compile_regexes("image_regexes")
+        self.filename_patterns = self._compile_regexes("filename_regexes")
 
         for key, value in kwargs.items():
+            error_obj = ValueError(
+                f"{self.__class__.__name__} got an unexpected keyword argument '{key}'"
+            )
             if key in ALLOWED_INIT_KWARGS and key in dir(self):
-                setattr(self, key, value)
+                if is_property(self, key):
+                    setattr(self, f"_{key}", value)
+                elif is_method(self, key):
+                    raise error_obj
+                else:
+                    setattr(self, key, value)
             else:
-                raise ValueError(
-                    f"{self.__class__.__name__} got an unexpected keyword argument '{key}'"
-                )
+                raise error_obj
+
+    def _compile_regexes(self, regex_attr: str) -> tuple[re.Pattern]:
+        regexes = getattr(self, regex_attr)
+        if regexes:
+            if isinstance(regexes, str):
+                regexes = (regexes,)
+            return tuple(re.compile(regexes, flags=re.VERBOSE) for regexes in regexes)
+        return ()
 
     @staticmethod
     def _metadata_to_nested_dict(
@@ -367,6 +368,7 @@ def _metadata_to_nested_dict(
         if isinstance(metadata, pd.DataFrame):
 
             def is_scalar(x) -> bool:
+                """Check if scalar because 'truth value of Series is ambigous'."""
                 return not hasattr(x, "__len__") or len(x) <= 1
 
             def na_to_none(x) -> None:
@@ -631,12 +633,24 @@ def _get_metadata_attributes(self, metadata_attributes: dict) -> dict:
 
     def _to_xarray(self, array: np.ndarray, transform: Affine) -> DataArray:
         """Convert the raster to  an xarray.DataArray."""
+        attrs = {"crs": self.crs}
+        for attr in set(self.metadata_attributes).union({"date"}):
+            try:
+                attrs[attr] = getattr(self, attr)
+            except Exception:
+                pass
+
         if len(array.shape) == 2:
             height, width = array.shape
             dims = ["y", "x"]
         elif len(array.shape) == 3:
             height, width = array.shape[1:]
             dims = ["band", "y", "x"]
+        elif not any(dim for dim in array.shape):
+            DataArray(
+                name=self.name or self.__class__.__name__,
+                attrs=attrs,
+            )
         else:
             raise ValueError(
                 f"Array should be 2 or 3 dimensional. Got shape {array.shape}"
@@ -644,13 +658,6 @@ def _to_xarray(self, array: np.ndarray, transform: Affine) -> DataArray:
 
         coords = _generate_spatial_coords(transform, width, height)
 
-        attrs = {"crs": self.crs}
-        for attr in set(self.metadata_attributes).union({"date"}):
-            try:
-                attrs[attr] = getattr(self, attr)
-            except Exception:
-                pass
-
         return DataArray(
             array,
             coords=coords,
@@ -667,7 +674,7 @@ class Band(_ImageBandBase):
     backend: str = "numpy"
 
     @classmethod
-    def from_gdf(
+    def from_geopandas(
         cls,
         gdf: GeoDataFrame | GeoSeries,
         res: int,
@@ -691,7 +698,7 @@ def from_gdf(
         )
 
         obj = cls(arr, res=res, crs=gdf.crs, bounds=gdf.total_bounds, **kwargs)
-        obj._from_gdf = True
+        obj._from_geopandas = True
         return obj
 
     def __init__(
@@ -839,12 +846,18 @@ def band_id(self) -> str:
     @property
     def height(self) -> int:
         """Pixel heigth of the image band."""
-        return self.values.shape[-2]
+        try:
+            return self.values.shape[-2]
+        except IndexError:
+            return 0
 
     @property
     def width(self) -> int:
         """Pixel width of the image band."""
-        return self.values.shape[-1]
+        try:
+            return self.values.shape[-1]
+        except IndexError:
+            return 0
 
     @property
     def tile(self) -> str:
@@ -892,7 +905,7 @@ def get_n_largest(
         copied = self.copy()
         value_must_be_at_least = np.sort(np.ravel(copied.values))[-n] - (precision or 0)
         copied._values = np.where(copied.values >= value_must_be_at_least, 1, 0)
-        df = copied.to_gdf(column).loc[lambda x: x[column] == 1]
+        df = copied.to_geopandas(column).loc[lambda x: x[column] == 1]
         df[column] = f"largest_{n}"
         return df
 
@@ -903,7 +916,7 @@ def get_n_smallest(
         copied = self.copy()
         value_must_be_at_least = np.sort(np.ravel(copied.values))[n] - (precision or 0)
         copied._values = np.where(copied.values <= value_must_be_at_least, 1, 0)
-        df = copied.to_gdf(column).loc[lambda x: x[column] == 1]
+        df = copied.to_geopandas(column).loc[lambda x: x[column] == 1]
         df[column] = f"smallest_{n}"
         return df
 
@@ -911,6 +924,9 @@ def clip(
         self, mask: GeoDataFrame | GeoSeries | Polygon | MultiPolygon, **kwargs
     ) -> "Band":
         """Clip band values to geometry mask."""
+        if not self.height or not self.width:
+            return self
+
         values = _clip_xarray(
             self.to_xarray(),
             mask,
@@ -978,7 +994,6 @@ def load(
         if self.has_array and [int(x) for x in bounds] != [int(x) for x in self.bounds]:
             print(self)
             print(self.mask)
-            print(self.mask.values.shape)
             print(self.values.shape)
             print([int(x) for x in bounds], [int(x) for x in self.bounds])
             raise ValueError(
@@ -1284,7 +1299,7 @@ def zonal(
             dropna=dropna,
         )
 
-    def to_gdf(self, column: str = "value") -> GeoDataFrame:
+    def to_geopandas(self, column: str = "value") -> GeoDataFrame:
         """Create a GeoDataFrame from the image Band.
 
         Args:
@@ -1328,17 +1343,35 @@ def _to_numpy(
         self, arr: np.ndarray | DataArray, masked: bool = True
     ) -> np.ndarray | np.ma.core.MaskedArray:
         if not isinstance(arr, np.ndarray):
+            mask_arr = None
             if masked:
+                # if self.mask is not None:
+                #     print(self.mask.values.shape, arr.shape)
+                # if self.mask is not None and self.mask.values.shape == arr.shape:
+                #     print("hei", self.mask.values.sum())
+                #     mask_arr = self.mask.values
+                # else:
+                #     mask_arr = np.full(arr.shape, False)
+                # try:
+                #     print("hei222", arr.isnull().values.sum())
+                #     mask_arr |= arr.isnull().values
+                # except AttributeError:
+                #     pass
+                # mask_arr = np.full(arr.shape, False)
                 try:
                     mask_arr = arr.isnull().values
                 except AttributeError:
-                    mask_arr = np.full(arr.shape, False)
+                    pass
             try:
                 arr = arr.to_numpy()
             except AttributeError:
                 arr = arr.values
+            if mask_arr is not None:
+                arr = np.ma.array(arr, mask=mask_arr, fill_value=self.nodata)
+
         if not isinstance(arr, np.ndarray):
             arr = np.array(arr)
+
         if (
             masked
             and self.mask is not None
@@ -1750,10 +1783,10 @@ def bounds(self) -> tuple[int, int, int, int] | None:
                 bounds.append(band.bounds)
             return get_total_bounds(bounds)
 
-    def to_gdf(self, column: str = "value") -> GeoDataFrame:
+    def to_geopandas(self, column: str = "value") -> GeoDataFrame:
         """Convert the array to a GeoDataFrame of grid polygons and values."""
         return pd.concat(
-            [band.to_gdf(column=column) for band in self], ignore_index=True
+            [band.to_geopandas(column=column) for band in self], ignore_index=True
         )
 
     def sample(
@@ -2491,7 +2524,7 @@ def to_xarray(
         return xr.combine_by_coords(list(xarrs.values()))
         # return Dataset(xarrs)
 
-    def to_gdfs(self, column: str = "value") -> dict[str, GeoDataFrame]:
+    def to_geopandas(self, column: str = "value") -> dict[str, GeoDataFrame]:
         """Convert each band in each Image to a GeoDataFrame."""
         out = {}
         i = 0
@@ -2504,7 +2537,7 @@ def to_gdfs(self, column: str = "value") -> dict[str, GeoDataFrame]:
                     name = f"{self.__class__.__name__}({i})"
 
                 if name not in out:
-                    out[name] = band.to_gdf(column=column)
+                    out[name] = band.to_geopandas(column=column)
         return out
 
     def sample(self, n: int = 1, size: int = 500) -> "ImageCollection":
@@ -3257,7 +3290,7 @@ def __str__(self) -> str:
             what = "that have been merged"
         elif self.instance._from_array:
             what = "from arrays"
-        elif self.instance._from_gdf:
+        elif self.instance._from_geopandas:
             what = "from GeoDataFrames"
         else:
             raise ValueError(self.instance)
diff --git a/tests/test_explore.py b/tests/test_explore.py
index 7b4cbd76..fdb01fc9 100644
--- a/tests/test_explore.py
+++ b/tests/test_explore.py
@@ -18,123 +18,6 @@
 
 path_sentinel = testdata + "/sentinel2"
 
-# np.set_printoptions(linewidth=400)
-
-if 0:
-    for p, bbox in zip(
-        [
-            # "S2A_MSIL2A_20230624T104621_N0509_R051_T32VPM_20230624T170454.SAFE",
-            "S2B_MSIL2A_20170826T104019_N0208_R008_T32VNM_20221207T150454.SAFE",
-            "S2B_MSIL2A_20230606T103629_N0509_R008_T32VNM_20230606T121204.SAFE",
-        ],
-        [
-            # (sg.to_gdf([11.0771105, 59.9483914], crs=4326).to_crs(25833).buffer(1500)),
-            (
-                sg.to_gdf([10.28173443, 60.16616654], crs=4326)
-                .to_crs(25833)
-                .buffer(1500)
-            ),
-            (
-                sg.to_gdf([10.28173443, 60.16616654], crs=4326)
-                .to_crs(25833)
-                .buffer(1500)
-            ),
-        ],
-        strict=False,
-    ):
-        paths = sg.helpers.get_all_files(
-            f"C:/Users/ort/OneDrive - Statistisk sentralbyrå/data/sentinel2/{p}"
-        )
-        img = sg.Sentinel2Image(
-            f"C:/Users/ort/OneDrive - Statistisk sentralbyrå/data/sentinel2/{p}",
-        )
-        for path in paths:
-
-            if "tif" not in path:
-                continue
-            raster = sg.Raster.from_path(path)
-            try:
-                centroid
-            except NameError:
-                centroid = raster.centroid
-            out = (
-                Path(r"C:\Users\ort\git\ssb-sgis\tests\testdata\raster\sentinel2")
-                / f"{p}"
-            ) / (Path(raster.name).stem + "_clipped.tif")
-            print(path)
-            print(out)
-            import os
-
-            os.makedirs(out.parent, exist_ok=True)
-
-            print(bbox)
-            raster = raster.load().clip(
-                bbox,  # boundless=False, masked=False
-            )
-            print(raster.bounds)
-            print(raster.values)
-            print(raster.values.shape)
-            print(np.sum(np.where(raster.values != 0, 1, 0)))
-
-            # sg.explore(raster.to_gdf())
-            raster.write(out)
-            print(raster.shape)
-            # print(raster)
-            # sg.explore(raster.to_gdf(), "value")
-if 0:
-    SENTINEL2_FILENAME_REGEX = r"""
-        ^(?P<tile>T\d{2}[A-Z]{3})
-        _(?P<date>\d{8}T\d{6})
-        _(?P<band>B[018][\dA])
-        (?:_(?P<resolution>\d+)m)?
-        .*
-        \..*$
-    """
-    path_sentinel = r"C:\Users\ort\git\ssb-sgis\tests\testdata\raster\sentinel2\S2A_MSIL2A_20230601T104021_N0509_R008_T32VNM_20230601T215503.SAFE"
-
-    cube = sg.DataCube.from_root(
-        path_sentinel, res=10, filename_regex=SENTINEL2_FILENAME_REGEX
-    )
-
-    band2 = cube["B02"].load(indexes=1)
-    band3 = cube["B03"].load(indexes=1)
-    band4 = cube["B04"].load(indexes=1)
-
-    sg.torchgeo.Sentinel2.filename_regex = SENTINEL2_FILENAME_REGEX
-    ds = sg.torchgeo.Sentinel2(path_sentinel)
-    ds.plot(ds[ds.bounds])
-
-    print(band2.values.shape)
-    print(band3.values.shape)
-    print(band4.values.shape)
-    # sg.explore(band2.to_gdf(), "value")
-
-    arr = np.array([band2.values, band3.values, band4.values])
-    arr = arr.reshape(arr.shape[1], arr.shape[2], arr.shape[0])
-
-    print(arr.shape)
-
-    import matplotlib.pyplot as plt
-
-    fig, ax = plt.subplots(1, 1, figsize=(4, 4))
-
-    import torch
-
-    image = torch.clamp(torch.tensor(arr) / 10000, min=0, max=1)
-
-    print(image)
-
-    ax.imshow(image)
-    ax.axis("off")
-
-    # Use bilinear interpolation (or it is displayed as bicubic by default).
-    # plt.imshow(arr, interpolation="nearest")
-    plt.show()
-
-    import folium
-
-    folium.raster_layers.ImageOverlay(arr, bounds=raster.bounds)
-
 
 def test_image_collection():
 
@@ -369,7 +252,6 @@ def main():
     from oslo import roads_oslo
 
     test_image_collection()
-    # test_torch()
     test_explore(points_oslo(), roads_oslo())
     # not_test_explore(points_oslo(), roads_oslo())
 
diff --git a/tests/test_image_collection.py b/tests/test_image_collection.py
index 998cfd7b..d8ca3b0f 100644
--- a/tests/test_image_collection.py
+++ b/tests/test_image_collection.py
@@ -297,7 +297,7 @@ def _test_ndvi(collection, type_should_be, cloudless: bool):
             # assert (ndvi.cmap) == "Greens"
 
             print("explore ndvi, masking=", img.masking)
-            gdf = ndvi.to_gdf()
+            gdf = ndvi.to_geopandas()
             gdf_sample = gdf.sample(min(n, len(gdf)))
             e = sg.explore(ndvi, gdf_sample=gdf_sample, column="value")
 
@@ -401,7 +401,7 @@ def not_test_sample():
         for img in collection.sample(1, size=size):
             for band in img:
                 band.load()
-                print(band.to_gdf().pipe(sg.sort_small_first).geometry.area.sum())
+                print(band.to_geopandas().pipe(sg.sort_small_first).geometry.area.sum())
         e = sg.explore(collection.sample(1, size=size))
 
     # low buffer resolution means the area won't be exactly this
@@ -423,7 +423,7 @@ def not_test_sample():
 
     print("as gdfs")
     e = sg.explore(
-        collection.sample(1, size=size).load().to_gdfs(),
+        collection.sample(1, size=size).load().to_geopandas(),
         column="value",
         return_explorer=True,
     )
@@ -976,7 +976,7 @@ def test_merge():
     sg.explore(merged_by_year)
     sg.explore(grouped_by_year_merged_by_band)
     sg.explore(merged_by_band)
-    df = merged_by_band.to_gdf()
+    df = merged_by_band.to_geopandas()
     assert (bounds := tuple(int(x) for x in df.total_bounds)) == (
         569631,
         6657859,
@@ -1235,7 +1235,7 @@ def test_cloud():
         # assert np.sum(cloud_band)
         assert isinstance(cloud_band, sg.Band), cloud_band
         assert cloud_band.values.shape == (299, 299), cloud_band.values.shape
-        cloud_polys = img.mask.to_gdf().geometry
+        cloud_polys = img.mask.to_geopandas().geometry
         sg.explore(cloud_polys)
         assert isinstance(cloud_polys, GeoSeries), type(cloud_polys)
 
@@ -1414,16 +1414,18 @@ def test_convertion():
     _from_array = sg.Band(arr, res=band.res, crs=band.crs, bounds=band.bounds)
     assert (shape := _from_array.values.shape) == (29, 29), shape
 
-    gdf = band.to_gdf(column="val")
-    from_gdf = sg.Band.from_gdf(gdf, res=band.res)
+    gdf = band.to_geopandas(column="val")
+    from_geopandas = sg.Band.from_geopandas(gdf, res=band.res)
 
-    e = sg.explore(from_gdf=from_gdf.to_gdf(), band=band.to_gdf(), gdf=gdf)
+    e = sg.explore(
+        from_geopandas=from_geopandas.to_geopandas(), band=band.to_geopandas(), gdf=gdf
+    )
     assert len(e._gdfs) == 3
     assert all(len(gdf) == 837 for gdf in e._gdfs), [len(gdf) for gdf in e._gdfs]
     assert all(int(gdf.area.sum()) == 898_5540 for gdf in e._gdfs), [
         int(gdf.area.sum()) for gdf in e._gdfs
     ]
-    assert (shape := from_gdf.values.shape) == (29, 29), shape
+    assert (shape := from_geopandas.values.shape) == (29, 29), shape
 
 
 @print_function_name