geowombat 2.2.0.1

Utilities for geospatial data

GeoWombat on Anaconda

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

>>> import geowombat as gw

Use a context manager and Xarray plotting to analyze processing chains

>>> # 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

>>> # 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.

>>> # 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.

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:

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:

conda install geowombat

or faster with mamba:

mamba install geowombat

Pip Install

GeoWombat is not on PyPi, but it can be installed with pip. We provide detailed instructions in our documentation.

Universal Install Via Docker

If you are having trouble installing geowombat, the surest way to get it up and running is with Docker containers. See the Dockerfile, or for more details instructions, see the guide on pygis.io.

Learning

If you are new to geospatial programming in Python please refer to pygis.io