Welcome to geeViz

GeeViz is a free open source Python package developed by RedCastle Resources for exploring and analyzing Earth observation data within the Google Earth Engine (GEE) platform. geeViz provides user-friendly functions to simplify complex data processing, analysis, and visualization tasks, acting as a toolkit to make working with GEE data more accessible.

Background

RedCastle Resources has been using Google Earth Engine since 2012 for production-oriented landscape monitoring. Until 2017, most of our GEE scripts were written in javaScript. We recognized using the Python API would be more versatile than javaScript, but there was no equivalent to the Code Editor to debug our outputs.

In 2014, RedCastle Resources began developing the Landscape Change Monitoring (LCMS) Viewer framework for the US Department of Agriculture Forest Service (FS) to visualize and deliver landscape change detection map outputs using GEE. This framework proved invaluable at communicating what LCMS was and how to interact with a large multi-temporal dataset.

In 2019, we first published the geeViz Python package. We opened the majority of the LCMS Viewer framework to become geeViz.geeView, providing the same functionality LCMS leveraged to anyone using GEE. The data processing libraries we used were also converted from javaScript to Python and included as additional modules:

• geeViz.getImagesLib module facilitates processing Landsat, Sentinel-2, MODIS, and other datasets using standardized methods that help avoid common mistakes most of us make when using GEE.

• geeViz.changeDetectionLib module facilitates using GEE’s temporal segmentation algorithms.

To date, there is no other Python package that facilitates visualizing data inside GEE using both Python scripts or notebooks.

geeViz works in a regular Python script, IPython, notebooks, Colab notebooks, Colab Enterprise notebooks, and Vertex AI Workbench notebooks.

We continue to actively maintain geeViz to facilitate our own continental-scale mapping projects such as:

• National Land Cover Database (NLCD) Tree Canopy Cover

• Landscape Change Monitoring System (LCMS)

geeViz serves as the primary package used in the LCMS training, jointly developed by RedCastle Resources and Google.

geeViz provides a number of functions for working with GEE data and avoiding common GEE pitfalls, including:

• Importing and exporting data

  • Helps avoid null value bugs and properly sets no data values when exporting to Google Cloud Storage or Drive

• Data filtering and manipulation

  • Helps avoid bugs with image date filtering when creating composites that span across the new year (common when working in the tropics or Southern Hemisphere)

  • Helps set resampling method properly for continuous spectral bands, while preserving nearest neighbor for QA bands

• Data analysis and visualization on a map user interface

  • Provides an interactive map interface complete with pixel-based querying and area-based zonal stats charting

Resources

• Check out the Installation section for further information, including how to install the package.

• Check out the Examples section for examples showing uses of geeViz.

• geeViz.geeView.mapper is based on the Landscape Change Monitoring System (LCMS) Viewer framework. It leverages the same code the LCMS viewer uses, providing the ability to easily view and explore GEE objects in a map environment.

Attention

Please note that this documentation website is still under development. Future releases will have more complete documentation.

Search geeViz Documentation

• Index

• Module Index

geeViz Overview

• Installation
  • Installing geeViz
  • Getting Started
• Overview
  • geeViz.geeView
  • geeViz.getImagesLib
  • geeViz.changeDetectionLib
  • geeViz.taskManagerLib
  • geeViz.assetManagerLib
  • geeViz.cloudStorageManagerLib
  • geeViz.gee2Pandas
  • geeViz.foliumView
  • geeViz.phEEnoViz
• Details
  • geeViz.geeView
    • RGB_to_hex()
    • baseDomain()
    • cleanAccessToken()
    • color_dict_maker()
    • getProject()
    • get_poly_gradient_ct()
    • hex_to_rgb()
    • isPortActive()
    • is_notebook()
    • linear_gradient()
    • mapper
      • mapper.port
      • mapper.proxy_url
      • mapper.refreshTokenPath
      • mapper.serviceKeyPath
      • mapper.project
      • mapper.turnOffLayersWhenTimeLapseIsOn
      • mapper.addAreaChartLayer()
      • mapper.addLayer()
      • mapper.addSelectLayer()
      • mapper.addTimeLapse()
      • mapper.centerObject()
      • mapper.clearMap()
      • mapper.clearMapCommands()
      • mapper.clearMapLayers()
      • mapper.populateAreaChartLayerSelect()
      • mapper.setCanReorderLayers()
      • mapper.setCenter()
      • mapper.setMapTitle()
      • mapper.setQueryBoxColor()
      • mapper.setQueryCRS()
      • mapper.setQueryDateFormat()
      • mapper.setQueryPrecision()
      • mapper.setQueryScale()
      • mapper.setQueryToInfoWindow()
      • mapper.setQueryToSidePane()
      • mapper.setQueryTransform()
      • mapper.setTitle()
      • mapper.setYLabelBreakLength()
      • mapper.setYLabelFontSize()
      • mapper.setYLabelMaxLength()
      • mapper.setYLabelMaxLines()
      • mapper.setZoom()
      • mapper.turnOffAllLayers()
      • mapper.turnOnAllLayers()
      • mapper.turnOnAutoAreaCharting()
      • mapper.turnOnInspector()
      • mapper.turnOnSelectionAreaCharting()
      • mapper.turnOnUserDefinedAreaCharting()
      • mapper.view()
    • polylinear_gradient()
    • refreshToken()
    • robustInitializer()
    • run_local_server()
    • serviceAccountToken()
    • setProject()
    • verified_initialize()
  • geeViz.getImagesLib
    • HoCalcAlgorithm2()
    • RefinedLee()
    • addDateBand()
    • addFullYearJulianDayBand()
    • addIndices()
    • addJulianDayBand()
    • addSAVIandEVI()
    • addSensorBand()
    • addSoilIndices()
    • addTCAngles()
    • addYearBand()
    • addYearFractionBand()
    • addYearJulianDayBand()
    • addYearYearFractionBand()
    • addZenithAzimuth()
    • applyBitMask()
    • applyCloudScoreAlgorithm()
    • cFmask()
    • cFmaskCloud()
    • cFmaskCloudShadow()
    • collectionToImage()
    • compositeDates()
    • compositeTimeSeries()
    • dailyMosaics()
    • defringeLandsat()
    • exportToAssetWrapper()
    • exportToCloudStorageWrapper()
    • exportToDriveWrapper()
    • fillEmptyCollections()
    • formatArgs()
    • getClimateWrapper()
    • getImageCollection()
    • getLandsat()
    • getModisData()
    • getProcessedLandsatScenes()
    • getProcessedModis()
    • getQABits()
    • getS1()
    • getS2()
    • getSentinel2()
    • getTasseledCap()
    • harmonizationChastain()
    • harmonizationRoy()
    • landsatCloudScore()
    • maskEmptyPixels()
    • maskS2clouds()
    • medoidMosaicMSD()
    • offsetImageDate()
    • rescale()
    • setNoData()
    • sieve()
    • simpleAddIndices()
    • simpleAddTCAngles()
    • simpleGetTasseledCap()
    • simpleTDOM2()
    • simpleWaterMask()
    • superSimpleGetS2()
    • toDB()
    • toNatural()
    • uniqueValues()
    • wrapDates()
  • geeViz.changeDetectionLib
    • batchFeatherCCDCImgs()
    • ccdcChangeDetection()
    • featherCCDCImgs()
    • predictCCDC()
    • simpleGetTimeImageCollection()
    • simpleLANDTRENDR()
  • geeViz.taskManagerLib
  • geeViz.assetManagerLib
    • listAssets()
  • geeViz.cloudStorageManagerLib
    • bucket_exists()
    • rename_blobs()
  • geeViz.gee2Pandas
  • geeViz.foliumView
  • geeViz.phEEnoViz
• Examples
  • Python script examples
  • Python notebook examples
    • Getting Started using geeViz
      • Starter geeViz geeView Notebook
        • Importing geeViz.geeView
        • Adding a GEE image (NLCD) to the Map and viewing and querying it
        • Visualizing multiple layers on the map
        • Introducing map query lookup tables and custom styles
        • Visualizing vectors and rasters on the map
        • Visualizing GEE layers as a local geojson vector
        • Using geeView’s Map.addTimeLapse function to visualize an imageCollection
    • Visualize multi-temporal imageCollections
      • LCMS Viewer Intro Notebook
        • First, we’ll take a look at the foundation of the geeViz.geeView module - the LCMS Data Explorer
        • Create your own LCMS Viewer
        • Time lapses enable quick visualization of multi-temporal imageCollections
    • Visualize Annual NLCD
      • Annual NLCD geeViz Viewer
        • View and explore NLCD Land Cover and Land Use
        • View and explore Annual NLCD Fractional Impervious Surface
        • View and explore Annual NLCD Spectral Change
    • Visualize MapBiomas Datasets
      • Visualizing MapBiomas Datasets
        • Make a basic viewer
        • Calculating and Exporting Statistics¶
        • Create Sankey Charts
    • Visualize Dynamic World
      • Visualizing Dynamic World
        • Make a basic viewer
        • Visualizing Dynamic World over time
        • Create Sankey Charts
    • Visualize the Global Land Cover Dataset
      • Visualizing Global Land Cover
        • Make a basic viewer
        • Calculating and Exporting Landcover Statistics¶
        • Create Sankey Charts
    • Visualize LCMS and LCMAP Outputs
      • LCMAP and LCMS Outputs Viewer
        • Bring in LCMAP outputs
        • Bring in LCMS GEE collections
    • Leverage geeViz.getImagesLib to consistently process Landsat data
      • Create High Quality Landsat Composites
        • Setting up numerous parameters available for processing Landsat data
        • Improving composite outputs by including Landsat 7
        • Improving cloud masking using the cloudScore method
        • Improving cloud shadow masking using TDOM
    • Interactively summarize data
      • Visualizing Area Summaries
        • Basic Area Charting
        • Line and Sankey Charts
        • Ways of adding area charting layers
        • Charting Non-Thematic Data
        • Charting Images
        • Charting Images Without Color and Name Properties
        • Charting Time Lapses
        • Charting Thematic Data without set properties
          • Setting properties for charting
        • Comparing map output versions
        • Other charting summary zone selection methods
    • Learn how geeViz compares to geeMap
      • A comparison between geeViz and geeMap’s map visualization capabilities
        • First, we’ll take a look at the two different map interfaces
        • Some methods are similar between the methods available
        • Using geeMap and geeViz together
    • Run LandTrendr
      • Run LandTrendr and Visualize Outputs
    • Visualize LCMS and LCMAP Outputs
      • LCMAP and LCMS Outputs Viewer
        • Bring in LCMAP outputs
        • Bring in LCMS GEE collections
    • Visualize CCDC Outputs
      • CCDC Visualization Notebook
        • Bring in CCDC data and set parameters
        • View and query raw CCDC outputs
        • Change detection using CCDC
        • Create fitted time series
        • Combining CCDC outputs
        • Combining CCDC Outputs for Change Detection
    • Crosswalk LCMS Levels
      • Learn to crosswalk LCMS datasets to different levels
        • First, we’ll take a look at the various levels for LCMS data
        • Learn how to crosswalk and symbolize LCMS products at a specific level
        • Crosswalk and visualize all LCMS products and levels
  • Other examples