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:round_pushpin: Fast, Accurate Python library for Raster Operations

:zap: Extensible with Numba

:fast_forward: Scalable with Dask

:confetti_ball: Free of GDAL / GEOS Dependencies

:earth_africa: General-Purpose Spatial Processing, Geared Towards GIS Professionals

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Xarray-Spatial implements common raster analysis functions using Numba and provides an easy-to-install, easy-to-extend codebase for raster analysis.

Installation

# via pip
pip install xarray-spatial

# via conda
conda install -c conda-forge xarray-spatial

Downloading our starter examples and data

Once you have xarray-spatial installed in your environment, you can use one of the following in your terminal (with the environment active) to download our examples and/or sample data into your local directory.

xrspatial examples : Download the examples notebooks and the data used.

xrspatial copy-examples : Download the examples notebooks but not the data. Note: you won't be able to run many of the examples.

xrspatial fetch-data : Download just the data and not the notebooks.

In all the above, the command will download and store the files into your current directory inside a folder named 'xrspatial-examples'.

xarray-spatial grew out of the Datashader project, which provides fast rasterization of vector data (points, lines, polygons, meshes, and rasters) for use with xarray-spatial.

xarray-spatial does not depend on GDAL / GEOS, which makes it fully extensible in Python but does limit the breadth of operations that can be covered. xarray-spatial is meant to include the core raster-analysis functions needed for GIS developers / analysts, implemented independently of the non-Python geo stack.

Our documentation is still under construction, but docs can be found here.

Raster-huh?

Rasters are regularly gridded datasets like GeoTIFFs, JPGs, and PNGs.

In the GIS world, rasters are used for representing continuous phenomena (e.g. elevation, rainfall, distance), either directly as numerical values, or as RGB images created for humans to view. Rasters typically have two spatial dimensions, but may have any number of other dimensions (time, type of measurement, etc.)

Supported Spatial Functions with Supported Inputs

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Classification

Name	NumPy xr.DataArray	Dask xr.DataArray	CuPy GPU xr.DataArray	Dask GPU xr.DataArray
Equal Interval	✅️	✅	✅	️
Natural Breaks	✅️			️
Reclassify	✅️	✅	✅	✅
Quantile	✅️	✅	✅	️

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Focal

Name	NumPy xr.DataArray	Dask xr.DataArray	CuPy GPU xr.DataArray	Dask GPU xr.DataArray
Apply	✅️	✅️
Hotspots	✅️	✅️	✅️
Mean	✅️	✅️	✅️
Focal Statistics	✅️	✅️	✅️

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Multispectral

Name	NumPy xr.DataArray	Dask xr.DataArray	CuPy GPU xr.DataArray	Dask GPU xr.DataArray
Atmospherically Resistant Vegetation Index (ARVI)	✅️	✅️	✅️	✅️
Enhanced Built-Up and Bareness Index (EBBI)	✅️	✅️	✅️	✅️
Enhanced Vegetation Index (EVI)	✅️	✅️	✅️	✅️
Green Chlorophyll Index (GCI)	✅️	✅️	✅️	✅️
Normalized Burn Ratio (NBR)	✅️	✅️	✅️	✅️
Normalized Burn Ratio 2 (NBR2)	✅️	✅️	✅️	✅️
Normalized Difference Moisture Index (NDMI)	✅️	✅️	✅️	✅️
Normalized Difference Vegetation Index (NDVI)	✅️	✅️	✅️	✅️
Soil Adjusted Vegetation Index (SAVI)	✅️	✅️	✅️	✅️
Structure Insensitive Pigment Index (SIPI)	✅️	✅️	✅️	✅️
True Color	✅️	️	✅️	️

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Pathfinding

Name	NumPy xr.DataArray	Dask xr.DataArray	CuPy GPU xr.DataArray	Dask GPU xr.DataArray
A* Pathfinding	✅️

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Proximity

Name	NumPy xr.DataArray	Dask xr.DataArray	CuPy GPU xr.DataArray	Dask GPU xr.DataArray
Allocation	✅️	✅
Direction	✅️	✅
Proximity	✅️	✅

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Raster to vector

Name	NumPy xr.DataArray	Dask xr.DataArray	CuPy GPU xr.DataArray	Dask GPU xr.DataArray
Polygonize	✅️

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Surface

Name	NumPy xr.DataArray	Dask xr.DataArray	CuPy GPU xr.DataArray	Dask GPU xr.DataArray
Aspect	✅️	✅️	✅️	⚠️
Curvature	✅️			⚠️
Hillshade	✅️	✅️
Slope	✅️	✅️	✅️	⚠️
Terrain Generation	✅️	✅️	✅️
Viewshed	✅️
Perlin Noise	✅️	✅️	✅️
Bump Mapping	✅️

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Zonal

Name	NumPy xr.DataArray	Dask xr.DataArray	CuPy GPU xr.DataArray	Dask GPU xr.DataArray
Apply	✅️	✅️
Crop	✅️
Regions
Trim	✅️
Zonal Statistics	✅️	✅️
Zonal Cross Tabulate	✅️	✅️

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Local

Name	NumPy xr.DataArray	Dask xr.DataArray	CuPy GPU xr.DataArray	Dask GPU xr.DataArray
Cell Stats	✅️
Combine	✅️
Lesser Frequency	✅️
Equal Frequency	✅️
Greater Frequency	✅️
Lowest Position	✅️
Highest Position	✅️
Popularity	✅️
Rank	✅️

Usage

Basic Pattern

import xarray as xr
from xrspatial import hillshade

my_dataarray = xr.DataArray(...)
hillshaded_dataarray = hillshade(my_dataarray)

Check out the user guide here.

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Dependencies

xarray-spatial currently depends on Datashader, but will soon be updated to depend only on xarray and numba, while still being able to make use of Datashader output when available.

Notes on GDAL

Within the Python ecosystem, many geospatial libraries interface with the GDAL C++ library for raster and vector input, output, and analysis (e.g. rasterio, rasterstats, geopandas). GDAL is robust, performant, and has decades of great work behind it. For years, off-loading expensive computations to the C/C++ level in this way has been a key performance strategy for Python libraries (obviously...Python itself is implemented in C!).

However, wrapping GDAL has a few drawbacks for Python developers and data scientists:

• GDAL can be a pain to build / install.
• GDAL is hard for Python developers/analysts to extend, because it requires understanding multiple languages.
• GDAL's data structures are defined at the C/C++ level, which constrains how they can be accessed from Python.

With the introduction of projects like Numba, Python gained new ways to provide high-performance code directly in Python, without depending on or being constrained by separate C/C++ extensions. xarray-spatial implements algorithms using Numba and Dask, making all of its source code available as pure Python without any "black box" barriers that obscure what is going on and prevent full optimization. Projects can make use of the functionality provided by xarray-spatial where available, while still using GDAL where required for other tasks.

Citation

Cite our code:

makepath/xarray-spatial, https://github.com/makepath/xarray-spatial, ©2020-2024.