The Caspian Sea. Image by NASA.

In celebration of World GIS Day 2019, Yale University launched the GIS Day Conference from November 13 to 15, bringing together data science experts and students to explore GIS applications in a range of fields. TC Chakraborty, PhD Candidate at the Yale School of Forestry and Environmental Studies and Researcher at Data-Driven Lab, led a workshop that introduced participants to geospatial analysis and algorithm development using Google Earth Engine’s Javascript API. The following Beginner’s Cookbook, created by Chakraborty on Google Developers, presents several types of geospatial data and key Earth Engine functions for analysis and visualization. The cookbook was originally created as a workshop during Yale-NUS Data 2.0 hackathon, and later updated for Yale GIS Day 2018 and 2019:


GIS or Geographic Information System is the collection, visualization, and analysis of geographical or spatial data. In this section, we will cover the data types commonly used in GIS applications.

Vector data

Vector data represent objects on the Earth’s surface using their longitude and latitude, as well as combinations of the pairs of coordinates (lines, polylines, polygons, etc.).

Point data

A pair of coordinates (longitude, latitude), that represents the location of points on the Earth’s surface.

Example: Location of drop boxes, landmarks, etc.



A series of points that represents a line (straight or otherwise) on the Earth’s surface.

Example: Center of roads, rivers, etc.



A series of points (vertices) that define the outer edge of a region. Example: Outlines of cities, countries, continents, etc.


Raster data

Raster data represent objects/variables on the Earth’s surface as a matrix of values, in the form of pixels, cells, or grids.

Layers and bands

A raster is an image with a matrix of values representing the values of some observed attribute. Bands of a raster correspond to different variables, usually using the same matrix structure.

Example: Spatial variability of temperature, elevation, rainfall, etc. over a region.


Image sources:

The Google Earth Engine platform

Introductory video

Code editor

What is Earth Engine?

  • A cloud-based platform for planetary scale geospatial analysis
  • Uses Google’s computational resources to reduce processing time
  • A massive archive of remote sensing data

The Earth Engine Code Editor (source:

Basic functions

Declaring variables

var variableName = ee.ContainerType(value);

A container object (usually in the form ee.SomeVariableType) is used to wrap a native JavaScript object so that Google’s servers can perform operations on it.

Centering the map

Map.setCenter(long, lat, zoomLevel);

Zoom level varies from 0 (no zoom) to 20 (highest zoom level)

Displaying metadata


The print operation is also useful for printing data and getting debugging info. Note: You cannot print more than 5,000 elements at once.

Adding a layer to the map


Common Earth Engine data types


var str = ee.String('This is a string. Or is it? It is.');


var num = ee.Number(5);


var arr = ee.Array([[5, 2, 3], [-2, 7, 10], [6, 6, 9]]);


var lis = ee.List([5, 'five', 6, 'six']);


var dict = ee.Dictionary({five: 5, six: 6});

And the fun stuff…

  • ee.Geometry
  • ee.Feature
  • ee.FeatureCollection
  • ee.Image
  • ee.ImageCollection

Declaring geometries


var poi = ee.Geometry.Point(0, 45);

Multi points

var multi = ee.Geometry.MultiPoint(0, 45, 5, 6, 70, -56);

Line string

var lineStr = ee.Geometry.LineString([[0, 45], [5, 6], [70, -56]]);

Multi-line string

var mLineStr =
ee.Geometry.MultiLineString([[[0, 45], [5, 6], [70, -56]], [[0, -45], [-5, -6], [-70, 56]]]);

Linear ring

var linRin = ee.Geometry.LinearRing(0, 45, 5, 6, 70, -56, 0, 45);


var rect = ee.Geometry.Rectangle(0, 0, 60, 30);


var poly = ee.Geometry.Polygon([[[0, 0], [6, 3], [5, 5], [-30, 2], [0, 0]]]);


var multiPoly =
ee.Geometry.MultiPolygon([ee.Geometry.Polygon([[0, 0], [6, 3], [5, 5], [-30, 2], [0, 0]]),
ee.Geometry.Polygon([[0, 0], [-6, -3], [-5, -5], [30, -2], [0, 0]])]);

Features and FeatureCollections

  • Features are geometries associated with specific properties.
  • Feature collections are groups of features.

Counties in the contiguous United States

Functions and mapping

A function is a set of instructions to perform a specific task:

function functionName(Arguments) {

Calling a function

var result = functionName(input);

Mapping a function over a collection

var result =;

Mapping a function over a collection applies the function to every element in the collection.

Common operations on geometries

Finding the area of a geometry

var geoArea = geometry.area();

By default, all units in Earth Engine are in meters.

Finding the length of a line

var linLen = lineString.length();

Finding the perimeter of a geometry

var geoPeri = geometry.perimeter();

Reducing number of vertices in geometry

var simpGeo = geometry.simplify(100);

Finding the centroid of a geometry

var centrGeo = geometry.centroid();

Creating buffer around a geometry

var buffGeo = geometry.buffer(100);

Finding the bounding rectangle of a geometry

var bounGeo = geometry.bounds();

Finding the smallest polygon that can envelope a geometry

var convexGeo = geometry.convexHull();

Finding common areas between two or more geometries

var interGeo = geometry1.intersection(geometry2);

Finding the area that includes two or more geometries

var unGeo = geometry1.union(geometry2);

Example: Geometry operations

Let’s run of some these operations over the the state of Connecticut, US using geometries of the public US counties feature collection available on Earth Engine:

// Set map center over the state of CT.
Map.setCenter(-72.6978, 41.6798, 8);
// Load US county dataset.
var countyData=ee.FeatureCollection('TIGER/2018/Counties');
// Filter the counties that are in Connecticut (more on filters later).
var countyConnect=countyData.filter(ee.Filter.eq('STATEFP', '09'));
// Get the union of all the county geometries in Connecticut.
var countyConnectDiss=countyConnect.union();
// Add the layer to the map with a specified color and layer name.
Map.addLayer(countyConnectDiss, {color: 'red'}, 'Chicago dissolved');
// Find the rectangle that emcompasses the southernmost, westernmost,
// easternmost, and northernmost points of the feature.
var bound = countyConnectDiss.geometry().bounds();
// Add the layer to the map with a specified color and layer name.
Map.addLayer(bound, {color: 'yellow'}, 'Bounds');
// Find the polygon covering the extremities of the feature.
var convex = countyConnectDiss.geometry().convexHull();
// Add the layer to the map with a specified color and layer name.
Map.addLayer(convex, {color: 'blue'}, 'Convex Hull');
// Find the area common to two or more features.
var intersect = bound.intersection(convex, 100);
// Add the layer to the map with a specified color and layer name.
Map.addLayer(intersect, {color: 'green'}, 'Bound and convex intersection');
// Find the area encompassing two or more features; number is the maximum
// error in meters.
var union = bound.union(convex, 100);
// Add the layer to the map with a specified color and layer name.
Map.addLayer(union, {color: 'purple'}, 'Bound and convex union');
// Find the difference between two geometries
var diff=bound.difference(convex, 100);
// Add the layer to the map with a specified color and layer name.
Map.addLayer(diff, {color: 'purple'}, 'Bound and convex difference');
// Find area of feature.
var ar = countyConnectDiss.geometry().area();
// Find length of line geometry (You get zero since this is a polygon).
var length = countyConnectDiss.geometry().length();
// Find permeter of feature.
var peri = countyConnectDiss.geometry().perimeter();

Example: Mapping over a feature collection

By mapping over a collection, one can apply the same operation on every element in a collection. For instance, let’s run the same geometry operations on every county in Connecticut:

// Set map center over the state of CT.
Map.setCenter(-72.6978, 41.6798, 8);
// Load US county dataset.
var countyData=ee.FeatureCollection('TIGER/2018/Counties');
// Filter the counties that are in Connecticut.
var countyConnect=countyData.filter(
  ee.Filter.eq('STATEFP', '09'));
// Add the layer to the map with a specified color and layer name.
Map.addLayer(countyConnect, {color: 'red'}, 'Original Collection');
// Define function.
function performMap(feature) {
 // Reduce number of vertices in geometry; the number is to specify maximum
 // error in meters. This is only for illustrative purposes, since Earth Engine
 // can handle up to 1 million vertices.
 var simple = feature.simplify(10000);
 // Find centroid of geometry.
 var center = simple.centroid();
 // Return buffer around geometry; the number represents the width of buffer,
 // in meters.
 return center.buffer(5000);
// Map function over feature collection.
var mappedCentroid =;
// Add the layer to the map with a specified color and layer name.
Map.addLayer(mappedCentroid, {color: 'blue'}, 'Mapped buffed centroids');

Operations on features

Creating a feature with a specific property value

var feat = ee.Feature(geometry, {Name: 'featureName', Size: 500});

Creating a feature from an existing feature, renaming a property

var featNew =['name'], ['descriptor']);

Extracting values of a property from a Feature

var featVal = feature.get('size');

Example: Feature operations

Let’s create a feature from scratch and play around with its properties:

// Create geometry.
var varGeometry = ee.Geometry.Polygon(0, 0, 40, 30, 20, 20, 0, 0);
// Create feature from geometry.
var varFeature = ee.Feature(varGeometry, {
 name: ['Feature name', 'Supreme'],
 size: [500, 1000]
// Get values of a property.
var arr=varFeature.get('size');
// Print variable.
// Select a subset of properties and rename them.
var varFeaturenew =['name'], ['descriptor']);
// Print variable.


Filtering by property values

var bFilter = ee.Filter.eq(propertyName, value);

or .neq , .gt , .gte , .lt , and .lte

Filtering based on maximum difference from a threshold

var diffFilter = ee.Filter.maxDifference(threshold, propertyName, value);

Filtering by text property

var txtFilter = ee.Filter.stringContains(propertyName, stringValue);

or .stringStartsWith, and .stringEndsWith

Filtering by a value range

var rangeFilter = ee.Filter.rangeContains(
  propertyName, stringValue, minValue, maxValue);

Filtering by specific property values

var listFilter = ee.Filter.listContains(
  propertyName, value1, propertyName2, value2);

.inList to test against a list of values

Filtering by date range

var dateFilter = ee.Filter.calendarRange(startDate, stopDate);

Filtering by particular days of the year

var dayFilter = ee.Filter.dayOfYear(startDay, stopDay);

Filtering by a bounding area

var boundsFilter = ee.Filter.bounds(geometryOrFeature);

Combining and inversing filters

var newFilterAnd = ee.Filter.and(listOfFilters);
var newFilterOr = ee.Filter.or(listOfFilters);
var inverseFilter = ee.Filter.not(filter);

Operations on images

Selecting the bands of an image

var band =;

Creating masks

var mask = image.eq(value);

or .neq or .gt or .gte or .lt or .lte

Applying image masks

var masked = image.updateMask(mask);

Performing pixelwise calculations

var results = image.add(value);

or .subtract , .multiply , .divide , .max , .min , .abs , .round , .floor , .ceil , .sqrt , .exp, .log, .log10, .sin , .cos , .tan , .sinh , .cosh , .tanh , .acos, .asin

Shift pixels of an image

newImage = oldImage.leftShift(valueOfShift);

or .rightShift

Reducing an image to a statistic for an area of interest

var outputDictionary = varImage.reduceRegion(reducer, geometry, scale);

Operations on image collections

Selecting the first n images in a collection (based on property)

var selectedImages = imCollection.limit(n, propertyName, order);

Selecting images based on particular properties

var selectedIm = imCollection.filterMetadata(propertyName, operator, value);

Operators include: “equals”, “less_than”, “greater_than”, “not_equals”, “not_less_than”, “not_greater_than”, “starts_with”, “ends_with”, “not_starts_with”, “not_ends_with”, “contains”, “not_contains”.

Selecting images within a date range

var selectedIm = imCollection.filterDate(startDate, stopDate);

Selecting images within a bounding geometry

var selectedIm = imCollection.filterBounds(geometry);

Performing pixelwise calculations for all images in a collection

var sumOfImages = imCollection.sum();

or product()max()min()mean()mode()median()count().

Alternatively, using reducers:

var sumOfImages = imCollection.reduce(ee.Reducer.sum());

Compositing images in collection with the last image on top

var mosaicOfImages = imCollection.mosaic();

Alternatively, using reducers:

var sumOfImages = imCollection.reduce(ee.Reducer.first());

Example: Image and image collection operations

Let’s analyze images over a region of interest (the counties of Connecticut).

// Set map center over the state of CT.
Map.setCenter(-72.6978, 41.6798, 8);
// Load the MODIS MYD11A2 (8-day LST) image collection.
var raw = ee.ImageCollection('MODIS/006/MYD11A2');
// Load US county dataset.
var countyData=ee.FeatureCollection('TIGER/2018/Counties');
// Filter the counties that are in Connecticut.
// This will be the region of interest for the image operations.
var roi=countyData.filter(ee.Filter.eq('STATEFP', '09'));
// Examine image collection.
// Select a band of the image collection using either indexing or band name.
var bandSel1 =;
var bandSel2 ='LST_Day_1km');
// Filter the image collection by a date range.
var filtered = raw.filterDate('2002-12-30', '2004-4-27');
// Print filtered collection.
// Limit the image collection to the first 50 elements.
var limited = raw.limit(50);
// Print collections.
// Calculate mean of all images (pixel-by-pixel) in the collection.
var mean = bandSel1.mean();
// Isolate image to region of interest.
var clipped = mean.clip(roi);
// mathematical operation on image pixels to convert from digital number
// of satellite observations to degree Celsius.
var calculate = clipped.multiply(0.02).subtract(273.15);
// Add the layer to the map with a specified color palette and layer name.
Map.addLayer(calculate, {min: 15, max: 20, palette: ['blue', 'green', 'red']}, 'LST');
// Select pixels in the image that are greater than 30.8.
var mask =;
// Add the mask to the map with a layer name.
Map.addLayer(mask, {}, 'mask');
// Use selected pixels to update the mask of the whole image.
var masked = clipped.updateMask(mask);
// Add the final layer to the map with a specified color palette and layer name.
  {min: 10, max: 30, palette: ['blue', 'green', 'red']}, 'LST_masked');

Exporting data

Exporting a collection to Google Drive, Earth Engine Asset, or Google Cloud

  collection: varImage, description: 'fileName', region: geometry, scale: 1000

or Export.image.toCloudStorage()Export.image.toAsset()Export.table.toDrive()Export.table.toCloudStorage()

Example: Importing and exporting data

// Function to find mean of pixels in region of interest.
var getRegions = function(image) {
  // Load US county dataset.
  var countyData = ee.FeatureCollection('TIGER/2018/Counties');
  // Filter the counties that are in Connecticut.
  // This will be the region of interest for the operations.
  var roi=countyData.filter(ee.Filter.eq('STATEFP', '09'));
  return image.reduceRegions({
    // Collection to run operation over.
    collection: roi,
    // Calculate mean of all pixels in region.
    reducer: ee.Reducer.mean(),
    // Pixel resolution used for the calculations.
    scale: 1000
// Load image collection, filter collection to date range, select band of
// interest, calculate mean of all images in collection, and multiply by
// scaling factor.
var image =
    .filterDate('2002-07-08', '2017-07-08')
// Print final image.
// Call function.
var coll = getRegions(image);
// Export image to Google Drive.
 collection: coll,
 description: 'NDVI_all',
 fileFormat: 'CSV'
// Print final collection.

Bonus: Timelapse example

// Timelapse example (based on google API example);
// Create rectangle over Dubai.
var geometry = ee.Geometry.Rectangle([55.1, 25, 55.4, 25.4]);
// Add layer to map.
// Load Landsat image collection.
var allImages = ee.ImageCollection('LANDSAT/LT05/C01/T1_TOA')
 // Filter row and path such that they cover Dubai.
 .filter(ee.Filter.eq('WRS_PATH', 160))
 .filter(ee.Filter.eq('WRS_ROW', 43))
 // Filter cloudy scenes.
 .filter('CLOUD_COVER', 30))
 // Get required years of imagery.
 .filterDate('1984-01-01', '2012-12-30')
 // Select 3-band imagery for the video.
 .select(['B4', 'B3', 'B2'])
 // Make the data 8-bit.
 .map(function(image) {
  return image.multiply(512).uint8();
 collection: allImages,
 // Name of file.
 description: 'dubaiTimelapse',
 // Quality of video.
 dimensions: 720,
 // FPS of video.
 framesPerSecond: 8,
 // Region of export.
 region: geometry

Dubai timelapse

Urban growth in Dubai

Example applications

What can you do with Google Earth Engine?

Additional resources