Filters

GeoUtils provides several filters to process raster data. They can be applied to Raster objects.

Available filters

The following filters are currently available in GeoUtils:

Filter Name	Description	Typical Effect
gaussian	Applies a Gaussian (blur) filter with a specified sigma.	Smooths the image, reduces noise while slightly blurring edges.
median	Applies a median filter over a sliding window.	Reduces noise while preserving edges better than Gaussian.
mean	Applies a mean (average) filter with a specified kernel size.	Smooths the image uniformly, reduces high-frequency noise.
max	Applies a maximum filter over a sliding window.	Enhances bright regions, expands high-intensity areas.
min	Applies a minimum filter over a sliding window.	Suppresses bright regions, expands dark regions.
distance	Removes pixels that deviate strongly from local neighborhood average (within a radius).	Removes outliers and anomalous values based on local context.
custom	Allows users to define their own filter function to be applied to a numpy array.

Parameters

Parameter	Definition	Available for filter	Type	Default value
engine	Filtering engine to use, either “scipy” or “numba”.	median	str	scipy
outlier_threshold	The minimum difference abs(array - mean) for a pixel to be considered an outlier	distance	float	2
radius	The radius in which the average value is calculated	distance	float	5
sigma	The sigma of the Gaussian kernel	gaussian	float	5
size	The size of the window to use (must be odd).	median, mean, min, max	int	5
kwargs	Kwargs from scipy are available	gaussian, min, max	dict

Note

The median filter can be computationally intensive, especially on large rasters. GeoUtils supports the use of Numba to accelerate filter computations. To enable Numba, ensure it is installed in your environment and set the engine parameter to numba when applying the filter

Applying filters

Filters can be applied to a Raster object using the filter() function. For example:

import geoutils as gu
filename_rast = gu.examples.get_path("exploradores_aster_dem")
rast = gu.Raster(filename_rast)
# Filter the raster with a median filter of size 5
rast_filtered = rast.filter("median", size=5)

/home/docs/checkouts/readthedocs.org/user_builds/geoutils/conda/latest/lib/python3.14/site-packages/scipy/ndimage/_filters.py:183: RuntimeWarning: All-NaN slice encountered
  return footprinted_function(xp.asarray(view), **kwargs)

Show the code for plotting the figure

Hide the code for plotting the figure

import matplotlib.pyplot as plt

f, ax = plt.subplots(1, 2)
ax[0].set_title("Original Raster")
rast.plot(ax=ax[0])
ax[1].set_title("Filtered raster")
rast_filtered.plot(ax=ax[1])
plt.tight_layout()

For example, to apply a median filter:

# Apply a median filter with a kernel size of 3
filtered_raster = gu.filters.median_filter(rast.data, size=3)

Users can also apply custom filters by providing a function that takes a 2D numpy array as input and returns a filtered 2D numpy array.

import numpy as np

# Filter the raster with a hand-made filter
def double_filter(arr: np.ndarray) -> np.ndarray:
    return arr * 2
rast_double = rast.filter(double_filter)