Creating training patches for Deep Learning Image Segmentation of Satellite (Sentinel 2) Imagery using the Google Earth Engine (GEE)

Figure — Example of patch creation for image segmentation of water bodies.

Update

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Introduction

In some previous stories (here, here and here) we’ve used PyTorch and Fast.ai library to segment clouds in satellite images, using as reference a public dataset (Kaggle’s 38-Cloud: Cloud Segmentation in Satellite Images). However, there are cases when we need to prepare our own dataset from the beginning, and that can be time-consuming without the proper tools.

As it is not my objective here to explain GEE in depth, I will cover just the basics needed to accomplish our final goal, that is to obtain training patches ready to be consumed by any deep learning framework. The workflow I will present here, was done for Sentinel-2 images, but can be easily modified for any other imagery available in the Google Earth Engine platform.

These are the steps:

• Select an image from a GEE image collection
• Perform a supervised image classification within GEE