A ninja python package that unifies the Google Earth Engine ecosystem.

Overview

ee_extra

A Python package that unifies the Google Earth Engine ecosystem.

EarthEngine.jl | rgee | rgee+ | eemont

PyPI conda-forge License Documentation Status Tests Awesome Spectral Indices GEE STAC Scale and Offset ee-appshot Black isort


GitHub: https://github.com/r-earthengine/ee_extra

Documentation: https://ee-extra.readthedocs.io

PyPI: https://pypi.python.org/pypi/ee_extra

Conda-forge: https://anaconda.org/conda-forge/ee_extra


Overview

Google Earth Engine (GEE) is a cloud-based service for geospatial processing of vector and raster data. The Earth Engine platform has a JavaScript and a Python API with different methods to process geospatial objects. Google Earth Engine also provides a HUGE PETABYTE-SCALE CATALOG of raster and vector data that users can process online.

There are a lot of fantastic third-party GEE packages and projects around GitHub. However, most of them are coded in JavaScript or Python, and they are not straightforward to translate to R, Julia, or other programming languages. The main goal of eeExtra is to guarantee a smooth import of these projects in other programming languages by standardizing different methods and enabling the use of JavaScript modules outside the Code Editor.

ee_extra_diagram

Some of the eeExtra features are listed here:

  • Automatic scaling and offsetting.
  • Spectral Indices computation (using Awesome Spectral Indices).
  • Clouds and shadows masking.
  • STAC related functions.

And the most important feature:

  • Enabling the usage of JavaScript modules outside the Code Editor.

How does it work?

eeExtra is a Python package, just like any other, but it is a ninja that serves as a methods provider for different environments: R, Julia and Python itself. eeExtra accomplish this by being the powerhouse of some amazing packages such as rgee, rgee+, and eemont.

Public JavaScript module can also be used outside the Code Editor in these packages through eeExtra. For this, eeExtra implements a rigorous JavaScript translation module that allows users to install, require and use JavaScript modules as if they were on the Code Editor!

You may be wondering "Why is it a ninja package?", well, that's a valid question, the whole point of eeExtra resides in the fact that nobody has to use eeExtra itself, but rather use one of the packages that are powered by eeExtra! :)

Installation

Install the latest version from PyPI:

pip install ee_extra

Install soft ee_extra dependencies:

pip install jsbeautifier regex

Upgrade eeExtra by running:

pip install -U ee_extra

Install the latest version from conda-forge:

conda install -c conda-forge ee_extra

Install the latest dev version from GitHub by running:

pip install git+https://github.com/r-earthengine/ee_extra

Features

Let's see some of the awesome features of eeExtra and how to use them from the powered packages in python and R!

Scale and Offset

Most datasets in the data catalog are scaled and in order to get their real values, we have to scale (and sometimes offset) them!

Python (eemont) R (rgee+) Julia (EarthEngine.jl)
import ee, eemont
ee.Initialize()
db = 'COPERNICUS/S2_SR'
S2 = ee.ImageCollection(db)
S2.scaleAndOffset()
library(rgee)
library(rgeeExtra)
ee_Initialize()
db <- 'COPERNICUS/S2_SR'
S2 <- ee$ImageCollection(db)
ee_extra_scaleAndOffset(S2)
using PyCall
using EarthEngine

Initialize()

ee_extra = pyimport("ee_extra")
ee_core = ee_extra.STAC.core
db = "COPERNICUS/S2_SR"
S2 = ee.ImageCollection(db)
ee_core.scaleAndOffset(S2)

Spectral Indices

Do you know the Awesome Spectral Indices? Well, you can compute them automatically with eeExtra!

Python (eemont) R (rgee+) Julia (EarthEngine.jl)
import ee, eemont
ee.Initialize()
db = 'COPERNICUS/S2_SR'
S2 = ee.ImageCollection(db)
S2 = S2.scaleAndOffset()
S2.spectralIndices("EVI")
library(rgee)
library(rgeeExtra)
ee_Initialize()
db <- 'COPERNICUS/S2_SR'
S2 <- ee$ImageCollection(db)
S2 <- ee_extra_scaleAndOffset(S2)
ee_extra_spIndices(S2, "EVI")
using PyCall
using EarthEngine

Initialize()

ee_extra = pyimport("ee_extra")
ee_core = ee_extra.STAC.core
ee_sp = ee_extra.Spectral.core
db = "COPERNICUS/S2_SR"
S2 = ee.ImageCollection(db)
S2 = ee_core.scaleAndOffset(S2)
ee_sp.spectralIndices(S2, "EVI")

STAC features

Access STAC properties easily!

Python (eemont) R (rgee+) Julia (EarthEngine.jl)
import ee, eemont
ee.Initialize()
db = 'COPERNICUS/S2_SR'
S2 = ee.ImageCollection(db)
S2.getSTAC()
library(rgee)
library(rgeeExtra)
ee_Initialize()
db <- 'COPERNICUS/S2_SR'
S2 <- ee$ImageCollection(db)
ee_extra_getSTAC()
  
using PyCall
using EarthEngine

Initialize()

ee_extra = pyimport("ee_extra")
ee_core = ee_extra.STAC.core
db = "COPERNICUS/S2_SR"
S2 = ee.ImageCollection(db)
ee_core.getSTAC(S2)

JavaScript Modules

This is perhaps the most important feature in eeExtra! What if you could use a JavaScript module (originally just useful for the Code Editor) in python or R? Well, wait no more for it!

  • JS Code Editor
var mod = require('users/sofiaermida/landsat_smw_lst:modules/Landsat_LST.js')

var geom = ee.Geometry.Rectangle(-8.91, 40.0, -8.3, 40.4)
var LST = mod.collection("L8", "2018-05-15", "2018-05-31", geom, true)

print(LST)
  • Python eemont
import ee, eemont

ee.Initialize()
module = 'users/sofiaermida/landsat_smw_lst:modules/Landsat_LST.js'
ee.install(module)
mod = ee.require(module)

geom = ee.Geometry.Rectangle(-8.91, 40.0, -8.3, 40.4)
LST = mod.collection("L8", "2018-05-15", "2018-05-31", geom, True)
print(LST)
  • R rgeeExtra
library(rgee)
library(rgeeExtra)

ee_Initialize()

lsmod <- 'users/sofiaermida/landsat_smw_lst:modules/Landsat_LST.js'
mod <- module(lsmod)

geom <- ee$Geometry$Rectangle(-8.91, 40.0, -8.3, 40.4)
LST <- mod$collection("L8", "2018-05-15", "2018-05-31", geom, TRUE)
print(LST)
  • Julia EarthEngine.jl
using PyCall
using EarthEngine

Initialize()

ee_extra = pyimport("ee_extra")
landsat_module = "users/sofiaermida/landsat_smw_lst:modules/Landsat_LST.js"
ee_extra.install(landsat_module)
lsmodule = ee_extra.require(landsat_module)

geom = Rectangle(-8.91, 40.0, -8.3, 40.4)
LST = lsmodule.collection("L8", "2018-05-15", "2018-05-31", geom, true)
print(LST)
Google maps for Jupyter notebooks

gmaps gmaps is a plugin for including interactive Google maps in the IPython Notebook. Let's plot a heatmap of taxi pickups in San Francisco: import g

Pascal Bugnion 747 Dec 19, 2022
ESMAC diags - Earth System Model Aerosol-Cloud Diagnostics Package

Earth System Model Aerosol-Cloud Diagnostics Package This Earth System Model (ES

Pacific Northwest National Laboratory 1 Jan 04, 2022
A modern, geometric typeface by @chrismsimpson (last commit @ 85fa625 Jun 9, 2020 before deletion)

Metropolis A modern, geometric typeface. Influenced by other popular geometric, minimalist sans-serif typefaces of the new millenium. Designed for opt

Darius 183 Dec 25, 2022
WhiteboxTools Python Frontend

whitebox-python Important Note This repository is related to the WhiteboxTools Python Frontend only. You can report issues to this repo if you have pr

Qiusheng Wu 304 Dec 15, 2022
Tool to display your current position and angle above your radar

🛠 Tool to display your current position and angle above your radar. As a response to the CS:GO Update on 1.2.2022, which makes cl_showpos a cheat-pro

Miko 6 Jan 04, 2023
Python tools for geographic data

GeoPandas Python tools for geographic data Introduction GeoPandas is a project to add support for geographic data to pandas objects. It currently impl

GeoPandas 3.5k Jan 03, 2023
Digital Earth Australia notebooks and tools repository

Repository for Digital Earth Australia Jupyter Notebooks: tools and workflows for geospatial analysis with Open Data Cube and xarray

Geoscience Australia 335 Dec 24, 2022
Spatial Interpolation Toolbox is a Python-based GUI that is able to interpolate spatial data in vector format.

Spatial Interpolation Toolbox This is the home to Spatial Interpolation Toolbox, a graphical user interface (GUI) for interpolating geographic vector

Michael Ward 2 Nov 01, 2021
Open Data Cube analyses continental scale Earth Observation data through time

Open Data Cube Core Overview The Open Data Cube Core provides an integrated gridded data analysis environment for decades of analysis ready earth obse

Open Data Cube 410 Dec 13, 2022
Asynchronous Client for the worlds fastest in-memory geo-database Tile38

This is an asynchonous Python client for Tile38 that allows for fast and easy interaction with the worlds fastest in-memory geodatabase Tile38.

Ben 53 Dec 29, 2022
A public data repository for datasets created from TransLink GTFS data.

TransLink Spatial Data What: TransLink is the statutory public transit authority for the Metro Vancouver region. This GitHub repository is a collectio

Henry Tang 3 Jan 14, 2022
A bot that tweets info and location map for new bicycle parking added to OpenStreetMap within a GeoJSON boundary.

Bike parking tweepy bot app A twitter bot app that searches for bicycle parking added to OpenStreetMap. Relies on AWS Lambda/S3, Python3, Tweepy, Flas

Angelo Trivisonno 1 Dec 19, 2021
This app displays interesting statistical weather records and trends which can be used in climate related research including study of global warming.

This app displays interesting statistical weather records and trends which can be used in climate related research including study of global warming.

0 Dec 27, 2021
Bacon - Band-limited Coordinate Networks for Multiscale Scene Representation

BACON: Band-limited Coordinate Networks for Multiscale Scene Representation Project Page | Video | Paper Official PyTorch implementation of BACON. BAC

Stanford Computational Imaging Lab 144 Dec 29, 2022
A simple python script that, given a location and a date, uses the Nasa Earth API to show a photo taken by the Landsat 8 satellite. The script must be executed on the command-line.

What does it do? Given a location and a date, it uses the Nasa Earth API to show a photo taken by the Landsat 8 satellite. The script must be executed

Caio 42 Nov 26, 2022
Search and download Copernicus Sentinel satellite images

sentinelsat Sentinelsat makes searching, downloading and retrieving the metadata of Sentinel satellite images from the Copernicus Open Access Hub easy

837 Dec 28, 2022
A Python package for delineating nested surface depressions from digital elevation data.

Welcome to the lidar package lidar is Python package for delineating the nested hierarchy of surface depressions in digital elevation models (DEMs). I

Qiusheng Wu 166 Jan 03, 2023
Google Maps keeps old satellite imagery around for a while – this tool collects what's available for a user-specified region in the form of a GIF.

google-maps-at-88-mph The folks maintaining Google Maps regularly update the satellite imagery it serves its users, but outdated versions of the image

Noah Doersing 111 Sep 27, 2022
A trivia questions about Europe

EUROPE TRIVIA QUIZ IN PYTHON Project Outline Ask user if he / she knows more about Europe. If yes show the Trivia main screen, else show the end Trivi

David Danso 1 Nov 17, 2021
Python bindings and utilities for GeoJSON

geojson This Python library contains: Functions for encoding and decoding GeoJSON formatted data Classes for all GeoJSON Objects An implementation of

Jazzband 765 Jan 06, 2023