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geowombat
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GeoWombat: Utilities for geospatial data
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Download README
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Download (.md)
 [](https://github.com/jgrss/geowombat/blob/main/LICENSE.txt) [](https://www.python.org) [](https://github.com/jgrss/geowombat/releases) [](https://github.com/jgrss/geowombat/actions/) [](https://shields.io/category/size) [](https://readthedocs.org/projects/geowombat/) [GeoWombat on Anaconda](https://anaconda.org/conda-forge/geowombat) [](https://anaconda.org/conda-forge/geowombat) [](https://anaconda.org/conda-forge/geowombat) [](https://anaconda.org/conda-forge/geowombat) [](https://anaconda.org/conda-forge/geowombat) ### GeoWombat: Utilities for geospatial data Like a wombat, GeoWombat has a simple interface (for raster I/O) with a strong backend (for data processing at scale). ## Common Remote Sensing Uses * Simple read/write for a variety of sensors, including: * Sentinel 2 * Landsat 5-8 * PlanetScope * Others * Image mosaicking * On-the-fly image transformations (reprojection) * Point / polygon raster sampling, extraction * Time series analysis * Band math (NDVI, Tasseled cap, EVI etc) * Image classification and regression * Radiometry (BRDF normalization) * Distributed processing ## Basic usage - Sentinel & Landsat ```python >>> import geowombat as gw ``` Use a context manager and Xarray plotting to analyze processing chains ```python >>> # Define satellite sensors (here, Landsat 7) >>> with gw.config.update(sensor='l7'): >>> >>> # Open images as Xarray DataArrays >>> with gw.open('LT05_L1TP_227083_20110123_20161011_01_T1.tif') as src: >>> >>> # Apply calculations using Xarray and Dask >>> results = src.sel(band=['blue', 'green', 'red']).mean(dim='band') >>> >>> # Check results by computing the task and plotting >>> results.gw.imshow() ``` Use a context manager to pass sensor information to geowombat methods ```python >>> # Set the sensor as Sentinel 2 >>> with gw.config.update(sensor='s2'): >>> >>> # Open a Sentinel 2 image >>> with gw.open('L1C_T20HPH_A002352_20151204T141125_MTD.tif') as src: >>> >>> # Use built-in normalization methods, such as the NDVI >>> ndvi = src.gw.ndvi(scale_factor=0.0001) >>> >>> # Check results by computing the task and plotting >>> ndvi.gw.imshow() ``` Computation scales easily over large datasets with minimal changes to the code. ```python >>> # Set a reference image to align to >>> with gw.config.update(ref_image='ref_image.tif'): >>> >>> # Open images as Xarray DataArrays >>> with gw.open('image_a.tif') as srca, gw.open('image_b.tif') as srcb: >>> >>> # The size of srca, srcb, and results are determined by the configuration context >>> results = srca.sel(band=1) * srcb.sel(band=[1, 2, 3]).mean(dim='band') >>> >>> # Initiate computation by writing the results to file. >>> # Compute the task in parallel using dask. >>> results.gw.save( >>> 'output.tif', >>> num_workers=4, >>> compress='lzw' >>> ) ``` ## Documentation For more details, see [https://geowombat.readthedocs.io](https://geowombat.readthedocs.io). ## Installation ### Conda Install To allow easy installation and build of all dependencies we recommend installing via conda-forge: Installing geowombat from the conda-forge channel can be achieved by adding conda-forge to your channels with: ```commandline conda config --add channels conda-forge conda config --set channel_priority strict ``` Once the conda-forge channel has been enabled, geowombat can be installed with conda: ```commandline conda install geowombat ``` or faster with mamba: ```commandline mamba install geowombat ``` ### Pip Install GeoWombat is on PyPI and can be installed with `pip`: ```commandline pip install geowombat ``` To include optional extras (e.g., `ml` and `stac`): ```commandline pip install "geowombat[ml,stac]" ``` Or to install all optional extras: ```commandline pip install "geowombat[all]" ``` We provide detailed instructions in our [documentation](https://geowombat.readthedocs.io/en/latest/install.html). ## Learning If you are new to geospatial programming in Python please refer to [pygis.io](https://pygis.io)
