Geemap Save

A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets.

Project README

====== geemap

.. image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://gishub.org/geemap-colab

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A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets.

Contents

  • Introduction_
  • Features_
  • Installation_
  • Usage_
  • Examples_
  • Dependencies_
  • Contributing_
  • References_
  • Credits_

Introduction

geemap is a Python package for interactive mapping with Google Earth Engine <https://earthengine.google.com/>__ (GEE), which is a cloud computing platform with a multi-petabyte catalog <https://developers.google.com/earth-engine/datasets/>__ of satellite imagery and geospatial datasets. During the past few years, GEE has become very popular in the geospatial community and it has empowered numerous environmental applications at local, regional, and global scales. GEE provides both JavaScript and Python APIs for making computational requests to the Earth Engine servers. Compared with the comprehensive documentation <https://developers.google.com/earth-engine>__ and interactive IDE (i.e., GEE JavaScript Code Editor <https://code.earthengine.google.com/>) of the GEE JavaScript API, the GEE Python API has relatively little documentation and limited functionality for visualizing results interactively. The geemap Python package was created to fill this gap. It is built upon ipyleaflet <https://github.com/jupyter-widgets/ipyleaflet> and ipywidgets <https://github.com/jupyter-widgets/ipywidgets>__, and enables users to analyze and visualize Earth Engine datasets interactively within a Jupyter-based environment.

geemap is intended for students and researchers, who would like to utilize the Python ecosystem of diverse libraries and tools to explore Google Earth Engine. It is also designed for existing GEE users who would like to transition from the GEE JavaScript API to Python API. The automated JavaScript-to-Python conversion module <https://github.com/giswqs/geemap/blob/master/geemap/conversion.py>__ of the geemap package can greatly reduce the time needed to convert existing GEE JavaScripts to Python scripts and Jupyter notebooks.

For video tutorials and notebook examples, please visit <https://geemap.org/tutorials>__. For complete documentation on geemap modules and methods, please visit <https://geemap.org/geemap>_.

If you find geemap useful in your research, please consider citing the following papers to support my work. Thank you for your support.

  • Wu, Q., (2020). geemap: A Python package for interactive mapping with Google Earth Engine. The Journal of Open Source Software, 5(51), 2305. <https://doi.org/10.21105/joss.02305>__
  • Wu, Q., Lane, C. R., Li, X., Zhao, K., Zhou, Y., Clinton, N., DeVries, B., Golden, H. E., & Lang, M. W. (2019). Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine. Remote Sensing of Environment, 228, 1-13. https://doi.org/10.1016/j.rse.2019.04.015 (pdf <https://gishub.org/2019_rse>_ | source code <https://doi.org/10.6084/m9.figshare.8864921>_)

Check out the geemap workshop I presented at the GeoPython Conference 2021. This workshop gives a comprehensive introduction to the key features of geemap.

.. image:: https://img.youtube.com/vi/wGjpjh9IQ5I/0.jpg :target: https://www.youtube.com/watch?v=wGjpjh9IQ5I

Features

Below is a partial list of features available for the geemap package. Please check the examples <https://github.com/giswqs/geemap/tree/master/examples>__ page for notebook examples, GIF animations, and video tutorials.

  • Convert Earth Engine JavaScripts to Python scripts and Jupyter notebooks.
  • Display Earth Engine data layers for interactive mapping.
  • Support Earth Engine JavaScript API-styled functions in Python, such as Map.addLayer(), Map.setCenter(), Map.centerObject(), Map.setOptions().
  • Create split-panel maps with Earth Engine data.
  • Retrieve Earth Engine data interactively using the Inspector Tool.
  • Interactive plotting of Earth Engine data by simply clicking on the map.
  • Convert data format between GeoJSON and Earth Engine.
  • Use drawing tools to interact with Earth Engine data.
  • Use shapefiles with Earth Engine without having to upload data to one's GEE account.
  • Export Earth Engine FeatureCollection to other formats (i.e., shp, csv, json, kml, kmz).
  • Export Earth Engine Image and ImageCollection as GeoTIFF.
  • Extract pixels from an Earth Engine Image into a 3D numpy array.
  • Calculate zonal statistics by group.
  • Add a customized legend for Earth Engine data.
  • Convert Earth Engine JavaScripts to Python code directly within Jupyter notebook.
  • Add animated text to GIF images generated from Earth Engine data.
  • Add colorbar and images to GIF animations generated from Earth Engine data.
  • Create Landsat timelapse animations with animated text using Earth Engine.
  • Search places and datasets from Earth Engine Data Catalog.
  • Use timeseries inspector to visualize landscape changes over time.
  • Export Earth Engine maps as HTML files and PNG images.
  • Search Earth Engine API documentation within Jupyter notebooks.
  • Import Earth Engine assets from personal account.
  • Publish interactive GEE maps directly within Jupyter notebook.
  • Add local raster datasets (e.g., GeoTIFF) to the map.
  • Perform image classification and accuracy assessment.
  • Extract pixel values interactively and export as shapefile and csv.

Installation

To use geemap, you must first sign up <https://earthengine.google.com/signup/>__ for a Google Earth Engine <https://earthengine.google.com/>__ account.

.. image:: https://i.imgur.com/ng0FzUT.png :target: https://earthengine.google.com

geemap is available on PyPI <https://pypi.org/project/geemap/>__. To install geemap, run this command in your terminal:

.. code:: python

pip install geemap

geemap is also available on conda-forge <https://anaconda.org/conda-forge/geemap>. If you have Anaconda <https://www.anaconda.com/distribution/#download-section> or Miniconda <https://docs.conda.io/en/latest/miniconda.html>__ installed on your computer, you can create a conda Python environment to install geemap:

.. code:: python

conda create -n gee python=3.9 conda activate gee conda install geopandas conda install mamba -c conda-forge mamba install geemap localtileserver -c conda-forge

Optionally, you can install Jupyter notebook extensions <https://github.com/ipython-contrib/jupyter_contrib_nbextensions>, which can improve your productivity in the notebook environment. Some useful extensions include Table of Contents, Gist-it, Autopep8, Variable Inspector, etc. See this post <https://towardsdatascience.com/jupyter-notebook-extensions-517fa69d2231> for more information.

.. code:: python

conda install jupyter_contrib_nbextensions -c conda-forge

If you have installed geemap before and want to upgrade to the latest version, you can run the following command in your terminal:

.. code:: python

pip install -U geemap

If you use conda, you can update geemap to the latest version by running the following command in your terminal:

.. code:: python

conda update -c conda-forge geemap

To install the development version from GitHub using Git <https://git-scm.com/>__, run the following command in your terminal:

.. code:: python

pip install git+https://github.com/giswqs/geemap

To install the development version from GitHub directly within Jupyter notebook without using Git, run the following code:

.. code:: python

import geemap geemap.update_package()

To use geemap in a Docker container, check out the following docker containers with geemap installed.

  • gee-community/ee-jupyter-contrib <https://github.com/gee-community/ee-jupyter-contrib/tree/master/docker/gcp_ai_deep_learning_platform>__
  • bkavlak/geemap <https://hub.docker.com/r/bkavlak/geemap>__
  • giswqs/geemap <https://hub.docker.com/r/giswqs/geemap>__

To use geemap in a Docker container, check out ee-jupyter-contrib <https://github.com/gee-community/ee-jupyter-contrib/tree/master/docker/gcp_ai_deep_learning_platform>__ or this page <https://hub.docker.com/r/bkavlak/geemap>__.

Usage

Important note: A key difference between ipyleaflet <https://github.com/jupyter-widgets/ipyleaflet>__ and folium <https://github.com/python-visualization/folium>__ is that ipyleaflet is built upon ipywidgets and allows bidirectional communication between the front-end and the backend enabling the use of the map to capture user input, while folium is meant for displaying static data only (source <https://blog.jupyter.org/interactive-gis-in-jupyter-with-ipyleaflet-52f9657fa7a>). Note that Google Colab <https://colab.research.google.com/> currently does not support ipyleaflet (source <https://github.com/googlecolab/colabtools/issues/60#issuecomment-596225619>). Therefore, if you are using geemap with Google Colab, you should use import geemap.foliumap <https://github.com/giswqs/geemap/blob/master/geemap/foliumap.py>. If you are using geemap with binder <https://mybinder.org/>__ or a local Jupyter notebook server, you can use import geemap <https://github.com/giswqs/geemap/blob/master/geemap/geemap.py>__, which provides more functionalities for capturing user input (e.g., mouse-clicking and moving).

More GEE Tutorials are available on my YouTube channel <https://www.youtube.com/c/QiushengWu>__.

|YouTube|

.. |YouTube| image:: https://wetlands.io/file/images/youtube.png :target: https://www.youtube.com/c/QiushengWu

To create an ipyleaflet-based interactive map:

.. code:: python

import geemap Map = geemap.Map(center=[40,-100], zoom=4) Map

To create a folium-based interactive map:

.. code:: python

import geemap.foliumap as geemap Map = geemap.Map(center=[40,-100], zoom=4) Map

To add an Earth Engine data layer to the Map:

.. code:: python

Map.addLayer(ee_object, vis_params, name, shown, opacity)

To center the map view at a given coordinates with the given zoom level:

.. code:: python

Map.setCenter(lon, lat, zoom)

To center the map view around an Earth Engine object:

.. code:: python

Map.centerObject(ee_object, zoom)

To add LayerControl to a folium-based Map:

.. code:: python

Map.addLayerControl()

To add a minimap (overview) to an ipyleaflet-based Map:

.. code:: python

Map.add_minimap()

To add additional basemaps to the Map:

.. code:: python

Map.add_basemap('Esri Ocean') Map.add_basemap('Esri National Geographic')

To add an XYZ tile layer to the Map:

.. code:: python

url = 'https://mt1.google.com/vt/lyrs=m&x={x}&y={y}&z={z}' Map.add_tile_layer(url, name='Google Map', attribution='Google')

To add a WMS layer to the Map:

.. code:: python

naip_url = 'https://services.nationalmap.gov/arcgis/services/USGSNAIPImagery/ImageServer/WMSServer?' Map.add_wms_layer(url=naip_url, layers='0', name='NAIP Imagery', format='image/png', shown=True)

To convert a shapefile to Earth Engine object and add it to the Map:

.. code:: python

ee_object = geemap.shp_to_ee(shp_file_path) Map.addLayer(ee_object, {}, 'Layer name')

To convert a GeoJSON file to Earth Engine object and add it to the Map:

.. code:: python

ee_object = geemap.geojson_to_ee(geojson_file_path) Map.addLayer(ee_object, {}, 'Layer name')

To download an ee.FeatureCollection as a shapefile:

.. code:: python

geemap.ee_to_csv(ee_object, filename, selectors)

To export an ee.FeatureCollection to other formats, including shp, csv, json, kml, and kmz:

.. code:: python

geemap.ee_export_vector(ee_object, filename, selectors)

To export an ee.Image as a GeoTIFF file:

.. code:: python

geemap.ee_export_image(ee_object, filename, scale, crs, region, file_per_band)

To export an ee.ImageCollection as GeoTIFF files:

.. code:: python

geemap.ee_export_image_collection(ee_object, output, scale, crs, region, file_per_band)

To extract pixels from an ee.Image into a 3D numpy array:

.. code:: python

geemap.ee_to_numpy(ee_object, bands, region, properties, default_value)

To import a 2D or 3D numpy array to an ee.Image using a given base coordinate reference system (crs) and transform between projected coordinates and the base:

.. code:: python

geemap.numpy_to_ee(np_array, crs, transform, transformWkt, band_names)

To import one or more variables from a netCDF file with a regular grid in EPSG:4326 to an ee.Image:

.. code:: python

geemap.netcdf_to_ee(nc_file, var_names, band_names, lon='lon', lat='lat')

To calculate zonal statistics:

.. code:: python

geemap.zonal_statistics(in_value_raster, in_zone_vector, out_file_path, statistics_type='MEAN')

To calculate zonal statistics by group:

.. code:: python

geemap.zonal_statistics_by_group(in_value_raster, in_zone_vector, out_file_path, statistics_type='SUM')

To create a split-panel Map:

.. code:: python

Map.split_map(left_layer='HYBRID', right_layer='ESRI')

To add a marker cluster to the Map:

.. code:: python

Map.marker_cluster() feature_collection = ee.FeatureCollection(Map.ee_markers)

To add a customized legend to the Map:

.. code:: python

legend_dict = { 'one': (0, 0, 0), 'two': (255,255,0), 'three': (127, 0, 127) } Map.add_legend(legend_title='Legend', legend_dict=legend_dict, position='bottomright') Map.add_legend(builtin_legend='NLCD')

To download a GIF from an Earth Engine ImageCollection:

.. code:: python

geemap.download_ee_video(tempCol, videoArgs, saved_gif)

To add animated text to an existing GIF image:

.. code:: python

geemap.add_text_to_gif(in_gif, out_gif, xy=('5%', '5%'), text_sequence=1984, font_size=30, font_color='#0000ff', duration=100)

To create a colorbar for an Earth Engine image:

.. code:: python

palette = ['blue', 'purple', 'cyan', 'green', 'yellow', 'red'] create_colorbar(width=250, height=30, palette=palette, vertical=False,add_labels=True, font_size=20, labels=[-40, 35])

To create a Landsat timelapse animation and add it to the Map:

.. code:: python

Map.add_landsat_ts_gif(label='Place name', start_year=1985, bands=['NIR', 'Red', 'Green'], frames_per_second=5)

To convert all GEE JavaScripts in a folder recursively to Python scripts:

.. code:: python

from geemap.conversion import * js_to_python_dir(in_dir, out_dir)

To convert all GEE Python scripts in a folder recursively to Jupyter notebooks:

.. code:: python

from geemap.conversion import * template_file = get_nb_template() py_to_ipynb_dir(in_dir, template_file, out_dir)

To execute all Jupyter notebooks in a folder recursively and save output cells:

.. code:: python

from geemap.conversion import * execute_notebook_dir(in_dir)

To search Earth Engine API documentation with Jupyter notebooks:

.. code:: python

import geemap geemap.ee_search()

To publish an interactive GEE map with Jupyter notebooks:

.. code:: python

Map.publish(name, headline, visibility)

To add a local raster dataset to the map:

.. code:: python

Map.add_raster(image, bands, colormap, layer_name)

To get image basic properties:

.. code:: python

geemap.image_props(image).getInfo()

To get image descriptive statistics:

.. code:: python

geemap.image_stats(image, region, scale)

To remove all user-drawn geometries:

.. code:: python

geemap.remove_drawn_features()

To extract pixel values based on user-drawn geometries:

.. code:: python

geemap.extract_values_to_points(out_shp)

To load a Cloud Optimized GeoTIFF as an ee.Image:

.. code:: python

image = geemap.load_GeoTIFF(URL)

To load a list of Cloud Optimized GeoTIFFs as an ee.ImageCollection:

.. code:: python

collection = geemap.load_GeoTIFFs(URLs)

Examples

The following examples require the geemap package, which can be installed using pip install geemap. Check the Installation_ section for more information. More examples can be found at another repo: A collection of 300+ Jupyter Python notebook examples for using Google Earth Engine with interactive mapping <https://github.com/giswqs/earthengine-py-notebooks>__.

  • Converting GEE JavaScripts to Python scripts and Jupyter notebooks_
  • Interactive mapping using GEE Python API and geemap_

Converting GEE JavaScripts to Python scripts and Jupyter notebooks ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Launch an interactive notebook with Google Colab. Keep in mind that the conversion might not always work perfectly. Additional manual changes might still be needed. ui and chart are not supported. The source code for this automated conversion module can be found at conversion.py <https://github.com/giswqs/geemap/blob/master/geemap/conversion.py>__.

.. image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/giswqs/geemap/blob/master/examples/notebooks/08_ee_js_to_ipynb.ipynb

.. code:: python

    import os
    from geemap.conversion import *

    # Create a temporary working directory
    work_dir = os.path.join(os.path.expanduser('~'), 'geemap')
    # Get Earth Engine JavaScript examples. There are five examples in the geemap package folder. 
    # Change js_dir to your own folder containing your Earth Engine JavaScripts, such as js_dir = '/path/to/your/js/folder'
    js_dir = get_js_examples(out_dir=work_dir) 

    # Convert all Earth Engine JavaScripts in a folder recursively to Python scripts.
    js_to_python_dir(in_dir=js_dir, out_dir=js_dir, use_qgis=True)
    print("Python scripts saved at: {}".format(js_dir))

    # Convert all Earth Engine Python scripts in a folder recursively to Jupyter notebooks.
    nb_template = get_nb_template()  # Get the notebook template from the package folder.
    py_to_ipynb_dir(js_dir, nb_template)

    # Execute all Jupyter notebooks in a folder recursively and save the output cells.
    execute_notebook_dir(in_dir=js_dir)

.. image:: https://i.imgur.com/8bedWtl.gif

Interactive mapping using GEE Python API and geemap ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Launch an interactive notebook with Google Colab. Note that Google Colab currently does not support ipyleaflet. Therefore, you should use import geemap.foliumap instead of import geemap.

.. image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/giswqs/geemap/blob/master/examples/notebooks/geemap_and_folium.ipynb

.. code:: python

    # Installs geemap package
    import subprocess

    try:
            import geemap
    except ImportError:
            print('geemap package not installed. Installing ...')
            subprocess.check_call(["python", '-m', 'pip', 'install', 'geemap'])

    # Checks whether this notebook is running on Google Colab
    try:
            import google.colab
            import geemap.foliumap as emap
    except:
            import geemap as emap

    # Authenticates and initializes Earth Engine
    import ee

    try:
            ee.Initialize()
    except Exception as e:
            ee.Authenticate()
            ee.Initialize()

    # Creates an interactive map
    Map = emap.Map(center=[40,-100], zoom=4)

    # Adds Earth Engine dataset
    image = ee.Image('USGS/SRTMGL1_003')

    # Sets visualization parameters.
    vis_params = {
            'min': 0,
            'max': 4000,
            'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5']}

    # Prints the elevation of Mount Everest.
    xy = ee.Geometry.Point([86.9250, 27.9881])
    elev = image.sample(xy, 30).first().get('elevation').getInfo()
    print('Mount Everest elevation (m):', elev)

    # Adds Earth Engine layers to Map
    Map.addLayer(image, vis_params, 'SRTM DEM', True, 0.5)
    Map.addLayer(xy, {'color': 'red'}, 'Mount Everest')
    Map.setCenter(100, 40, 4)
    # Map.centerObject(xy, 13)

    # Display the Map
    Map.addLayerControl()
    Map

.. image:: https://i.imgur.com/7NMQw6I.gif

Dependencies

  • bqplot <https://github.com/bloomberg/bqplot>__
  • colour <https://github.com/vaab/colour>__
  • earthengine-api <https://github.com/google/earthengine-api>__
  • folium <https://github.com/python-visualization/folium>__
  • geeadd <https://github.com/samapriya/gee_asset_manager_addon>__
  • geocoder <https://github.com/DenisCarriere/geocoder>__
  • ipyfilechooser <https://github.com/crahan/ipyfilechooser>__
  • ipyleaflet <https://github.com/jupyter-widgets/ipyleaflet>__
  • ipynb-py-convert <https://github.com/kiwi0fruit/ipynb-py-convert>__
  • ipytree <https://github.com/QuantStack/ipytree>__
  • ipywidgets <https://github.com/jupyter-widgets/ipywidgets>__
  • mss <https://github.com/BoboTiG/python-mss>__
  • pillow <https://github.com/python-pillow/Pillow>__
  • pyshp <https://github.com/GeospatialPython/pyshp>__
  • xarray-leaflet <https://github.com/davidbrochart/xarray_leaflet>__

Contributing

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

You can contribute in many ways:

Report Bugs ^^^^^^^^^^^

Report bugs at https://github.com/giswqs/geemap/issues.

If you are reporting a bug, please include:

  • Your operating system name and version.
  • Any details about your local setup that might be helpful in troubleshooting.
  • Detailed steps to reproduce the bug.

Fix Bugs ^^^^^^^^

Look through the GitHub issues for bugs. Anything tagged with "bug" and "help wanted" is open to whoever wants to implement it.

Implement Features ^^^^^^^^^^^^^^^^^^

Look through the GitHub issues for features. Anything tagged with "enhancement" and "help wanted" is open to whoever wants to implement it.

Write Documentation ^^^^^^^^^^^^^^^^^^^

geemap could always use more documentation, whether as part of the official geemap docs, in docstrings, or even on the web in blog posts, articles, and such.

Submit Feedback ^^^^^^^^^^^^^^^

The best way to send feedback is to file an issue at https://github.com/giswqs/geemap/issues.

If you are proposing a feature:

  • Explain in detail how it would work.
  • Keep the scope as narrow as possible, to make it easier to implement.
  • Remember that this is a volunteer-driven project, and that contributions are welcome :)

Get Started! ^^^^^^^^^^^^

Ready to contribute? Here's how to set up geemap for local development.

  1. Fork the geemap repo on GitHub.

  2. Clone your fork locally::

    $ git clone [email protected]:your_name_here/geemap.git

  3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development::

    $ mkvirtualenv geemap $ cd geemap/ $ python setup.py develop

  4. Create a branch for local development::

    $ git checkout -b name-of-your-bugfix-or-feature

    Now you can make your changes locally.

  5. When you're done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox::

    $ flake8 geemap tests $ python setup.py test or pytest $ tox

    To get flake8 and tox, just pip install them into your virtualenv.

  6. Commit your changes and push your branch to GitHub::

    $ git add . $ git commit -m "Your detailed description of your changes." $ git push origin name-of-your-bugfix-or-feature

  7. Submit a pull request through the GitHub website.

Pull Request Guidelines ^^^^^^^^^^^^^^^^^^^^^^^

Before you submit a pull request, check that it meets these guidelines:

  1. The pull request should include tests.
  2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
  3. The pull request should work for Python 3.6, 3.7 and 3.8, and for PyPy. Check https://travis-ci.com/giswqs/geemap/pull_requests and make sure that the tests pass for all supported Python versions.

Tips ^^^^

To run a subset of tests::

$ python -m unittest tests.test_geemap

Deploying ^^^^^^^^^

A reminder for the maintainers on how to deploy. Make sure all your changes are committed (including an entry in HISTORY.rst). Then run::

$ bump2version patch # possible: major / minor / patch $ git push $ git push --tags

Travis will then deploy to PyPI if tests pass.

References

To support my work, please consider citing the following articles:

  • Wu, Q., (2020). geemap: A Python package for interactive mapping with Google Earth Engine. The Journal of Open Source Software, 5(51), 2305. https://doi.org/10.21105/joss.02305
  • Wu, Q., Lane, C. R., Li, X., Zhao, K., Zhou, Y., Clinton, N., DeVries, B., Golden, H. E., & Lang, M. W. (2019). Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine. Remote Sensing of Environment, 228, 1-13. https://doi.org/10.1016/j.rse.2019.04.015 (pdf <https://gishub.org/2019_rse>_ | source code <https://doi.org/10.6084/m9.figshare.8864921>_)

Credits

This package was created with Cookiecutter <https://github.com/cookiecutter/cookiecutter>__ and the audreyr/cookiecutter-pypackage <https://github.com/audreyr/cookiecutter-pypackage>__ project template.

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