Who can use this feature
Editors of a project within a Professional or Pro + workspace.
Selection requirements for tool to work
A drone data classification OR
A polygon layer
AND
A Sentinel-2 image over the same area
TEMPORARILY UNAVAILABLE
Predict Density helps you quickly understand how much of a feature (e.g. woody vegetation, bare ground) exists across a much larger area than your drone map alone can cover. By combining the fine detail from your drone classification (or any polygon layer) with broader-scale Sentinel-2 satellite imagery, the tool lets you “scale up” what you see locally to the surrounding landscape.
To make sure the satellite data are clean and reliable, the tool automatically removes any Sentinel-2 pixels affected by clouds, shadows, snow, or other poor-quality conditions. This means the predictions are made only from good-quality satellite observations, giving you more trustworthy results.
Once both datasets are aligned, the tool measures how much of your chosen feature sits inside each area equivalent to a Sentinel-2 pixel. These real measurements are used to train a Random Forest regression model (a type of machine-learning algorithm) to learn the relationship between your high-resolution drone data and the coarser satellite data.
The end result is a map predicting the density or percent coverage of your feature across the entire Sentinel-2 scene. This allows you to extend your detailed drone insights to much larger regions, saving time, reducing field effort, and giving you a scalable way to monitor change, support reporting, or guide management decisions.
Create a classification with your drone data, or use the polygon tool to digitise features of interest (e.g. woody vegetation)
Stream a Sentinel-2 image covering the same area into your project
Select a category within your drone classification OR select your polygon layer
Use CTRL or CMD to multi-select your Sentinel-2 scene
From the top menu bar, click the Predict density tool
Top tips
Make sure that you choose a cloud-free Sentinel-2 image
Choose a Sentinel-2 image as close in time to the drone data as possible
Spend time getting your input classification or polygon layer as accurate as possible. The prediction can only be as accurate as the input, and errors will be amplified.
Known errors
Input extent is too large - please keep the area covered by your input polygons <300ha
Insufficient overlap between input training data and Sentinel-2 image - make sure that these cover the same area
If the overlapping area in the Sentinel-2 image is covered in cloud or cloud shadow, it will not return a result. Make sure that the satellite image you choose is cloud free.