Package: geeml
GEEML: Google Earth Engine Machine learningA python package to extract gee data for machine learning.Explore the documentation »View Demo·Report Bug·Request FeatureTable of ContentsAbout The ProjectGetting StartedInstallationBasic UsageRoadmapContributingLicenseContactAcknowledgmentsAbout The ProjectThis python package makes it easier to extract satellite data from Google Earth Engine using parallel processing and the Google Earth Engine high volume end point.In its current state it supports the extraction of data for traditional machine learning (tabular data) in the form of csv's and the extraction of GeoTiff image patches for Deep Neural Networks.MotivationThe Machine learning capabilities in the GEE JS code editor remain limited. For example, there is no support for XGBoost, LightGBM, NGBoost, etc. Moreover, the python ecosystem has much more support for training, valdation and hyperparameter tuning. However, for this functionality to be leveraged, data needs to be downloaded locally or stored in Google Drive or Google Cloud Storage to benefit from the Machine learning python ecosystem. Therfore, this package aims to make it easier and faster to download GEE-processed data in a machine learning-ready format.FeaturesParallel export of images or sparse images (for example, GEDI).Export raster values at points or polygons (ee.FeatureCollection).Summarise raster data within polygons (ee.FeatureCollections).Extract both tabular and Deep Neural Network (DNN) type datasets.Getting StartedInstallationTo install this package:pippipinstallgeemlBuild from source (latest version)pipinstallgit+https://github.com/Geethen/geeml.git(back to top)Basic usageDownload the NASADEM elevation data for Kenya.#import packagesimporteefromgeeml.utilsimportgetCountryfromgeeml.extractimportextractor# Authenticate GEEee.Authenticate()# Initialize GEE with high-volume end-pointee.Initialize(opt_url='https://earthengine-highvolume.googleapis.com')# Import datasets from GEEnasadem=ee.Image("NASA/NASADEM_HGT/001")# A point in Kenyapoi=ee.Geometry.Point([37.857884,-0.002197])kenya=getCountry(poi)# Download directorydd='/content/drive/MyDrive/geeml_example'# Prepare for data extractiontrialExtractor=extractor(covariates=nasadem,aoi=kenya,scale=5000,dd=dd)# Extract datatrialExtractor.extractAoi()For more examples, please refer to theDocumentation(back to top)RoadmapSupport the export of additional formats (TFrecords)Download data from GEE based on local shapefilesAdd more examples for using the packageSee theopen issuesfor a full list of proposed features (and known issues).(back to top)ContributingContributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make aregreatly appreciated.If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull Request(back to top)LicenseDistributed under the MIT License. SeeLICENSE.txtfor more information.(back to top)ContactGeethen Singh -@Geethen-geethen.singh@gmail.comProject Link:https://github.com/Geethen/geeml(back to top)AcknowledgmentsNatural StateUniversity of the WitwatersrandFitz patrick centre for african ornithologyThis package was created withCookiecutterand thegiswqs/pypackageproject template.This package uses thegeedimpackage for extracting image data at an AOI(back to top)