geeViz Documentation

• geeViz is a Python package that helps with data processing, analysis, and visualization in Google Earth Engine (GEE).

• geeViz provides a number of functions for working with GEE data, including:

  • Importing and exporting data

  • Data filtering and manipulation

  • Data analysis and visualization

  • Creating and managing maps

• geeViz works inside and outside Python notebook environments, providing a flexible method for visualizing data in GEE using Python

• 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
  • Other examples