ee.FeatureCollection.draw
Paints a vector collection for visualization. Not intended for use as input to other algorithms.
Usage | Returns |
---|
FeatureCollection.draw(color, pointRadius, strokeWidth) | Image |
Argument | Type | Details |
---|
this: collection | FeatureCollection | The collection to draw. |
color | String | A hex string in the format RRGGBB specifying the color to use for drawing the features. |
pointRadius | Integer, default: 3 | The radius in pixels of the point markers. |
strokeWidth | Integer, default: 2 | The width in pixels of lines and polygon borders. |
Examples
// FeatureCollection of power plants in Belgium.
var fc = ee.FeatureCollection('WRI/GPPD/power_plants')
.filter('country_lg == "Belgium"');
// Paint FeatureCollection to an image for visualization.
var fcVis = fc.draw({color: '800080', pointRadius: 5, strokeWidth: 3});
Map.setCenter(4.56, 50.78, 8);
Map.addLayer(fcVis);
Python setup
See the
Python Environment page for information on the Python API and using
geemap
for interactive development.
import ee
import geemap.core as geemap
# FeatureCollection of power plants in Belgium.
fc = ee.FeatureCollection('WRI/GPPD/power_plants').filter(
'country_lg == "Belgium"'
)
# Paint FeatureCollection to an image for visualization.
fc_vis = fc.draw(color='800080', pointRadius=5, strokeWidth=3)
m = geemap.Map()
m.set_center(4.56, 50.78, 8)
m.add_layer(fc_vis)
m
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[[["`draw()` visualizes FeatureCollections as images for display purposes, not for algorithmic input."],["It accepts color, point radius, and stroke width parameters to customize the visualization."],["Use `draw()` with FeatureCollections to create image overlays for maps, as demonstrated with the power plants example."]]],["The `FeatureCollection.draw()` method visualizes a feature collection as an image. It accepts a `FeatureCollection`, a `color` (hex string), `pointRadius` (integer, default 3), and `strokeWidth` (integer, default 2). The method returns an image and is intended for visualization, not algorithmic input. The examples demonstrate how to apply this method using the power plants of Belgium. It can be done in JavaScript or python (colab or not) and the visualization will be an image.\n"]]