geetools 1.0.0a3

A collection of tools to work with Google Earth Engine Python API

:warning: Warning :warning:
This package is under heavy refactoring. If you want to access the previous version, please have a look to the 0.x branch We also start to make pre-release so people can try the latest version. Have a look to the pypi page to stay up-to-date.
:warning: Warning :warning:

Google Earth Engine tools

These are a set of tools for working with Google Earth Engine Python API that may help to solve or automate some processes.

There is JavaScript module that you can import from the code editor that has similar functions (not exactly the same) and it's available here

Note for old users

New version 0.3.0

I have split this package in two. This geetools will contain functions and methods related to Google Earth Engine exclusively, so you can use this module in any python environment you like. For working in Jupyter I have made another package called ipygee available here

New version 0.5.0 (breaking changes)

I have split this package in two (again). Now the functions to make a strip of images using Pillow is available as a different package called geepillow

New version 0.6.0 (breaking changes)

I have split this package in two (again x2). The module geetools.collection in an independent package called geedataset

Installation

pip install geetools

Upgrade

pip install --upgrade geetools

Basic Usage

Export every image in a ImageCollection

import ee
ee.Initialize()
import geetools

# ## Define an ImageCollection
site = ee.Geometry.Point([-72, -42]).buffer(1000)
collection = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR").filterBounds(site).limit(5)

# Set parameters
bands = ['B2', 'B3', 'B4']
scale = 30
name_pattern = '{sat}_{system_date}_{WRS_PATH:%d}-{WRS_ROW:%d}'
## the keywords between curly brackets can be {system_date} for the date of the
## image (formatted using `date_pattern` arg), {id} for the id of the image
## and/or any image property. You can also pass extra keywords using the `extra`
## argument. Also, numeric values can be formatted using a format string (as
## shown in {WRS_PATH:%d} (%d means it will be converted to integer)
date_pattern = 'ddMMMy' # dd: day, MMM: month (JAN), y: year
folder = 'MYFOLDER'
data_type = 'uint32'
extra = dict(sat='L8SR')
region = site

# ## Export
tasks = geetools.batch.Export.imagecollection.toDrive(
            collection=collection,
            folder=folder,
            region=site,
            namePattern=name_pattern,
            scale=scale,
            dataType=data_type,
            datePattern=date_pattern,
            extra=extra,
            verbose=True,
            maxPixels=int(1e13)
        )

Some useful functions

batch exporting

• Export every image in an ImageCollection to Google Drive, GEE Asset or Cloud Storage examples
• Clip an image using a FeatureCollection and export the image inside every Feature example

Image processing

• Pansharp example
• Mask pixels around masked pixels (buffer around a mask) example
• Get the percentage of masked pixels inside a geometry example
• Cloud masking functions example

Compositing

• Closest date composite: replace masked pixels with the "last available not masked pixel" example
• Medoid composite example

Image Collections

• Mosaic same day example

Visualization

• Get visualization parameters using a stretching function example

All example Jupyter Notebooks

Jupyter Notebooks avilables here

Contributing

Any contribution is welcome. Any bug or question please use the github issue tracker

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