Intro to deep learning to track deforestation in supply chains
What’s included in this article
- What is Deep Learning?
- Measuring deforestation in extractive supply chains with ML
- When to build a custom model outside of Earth Engine?
- How to build a model with Google Cloud & Earth Engine?
- Try it out!
What is Deep Learning?
Out of the many ML algorithms out there, I’m happy to share that deep learning or artificial neural networks is a technique that can be used for almost any supervised learning job.
Deep learning approaches problems differently
David Cavazos, Developer Programs Engineer
Measuring deforestation in extractive supply chains with ML
So let’s say we would like to measure deforestation using deep learning; to get started with building a model we first need a dataset that includes satellite images with an even amount of labels marking where there are trees and where there aren’t. Next, we choose a goal, here are a few common ones. In our case, we simply want to know if there are trees or not for every pixel, and so this would be a binary semantic segmentation problem.
When to build a custom model outside of Earth Engine
So now that we covered what is deep learning, the next step is understanding which tools to use to build our deforestation model. For starters it’s important to call out that Google Earth Engine is a wonderful tool that helps organizations of all sizes find insights about changes on the planet, in order to make a climate positive impact. It has built-in machine learning algorithms (classifiers) that let users quickly spin them up, with just a basic machine learning background. This is fantastic place to start when using ML on geospatial data, however there are multiple situations where you will want to opt to build a custom model such as:
- You want to use a popular ML library such as TensorFlow Keras.
- You wish to build a state of the art model to build a global and accurate land cover map product such as Google’s Dynamic World.
- Or because you generally have too much data to process that you can’t execute it in just one task in Earth Engine (and are trying to figure out hacky ways to export your data).
Whenever you identify with any of these options, you will want to roll up your sleeves and dive into building a custom model, which does require expertise and of course working with multiple products. But I have good news, using deep learning is a great go-to algorithm.
How to build a model with Google Cloud & Earth Engine?
To get started, you will need an account with Google Earth Engine which is free for non-commercial entities and Google Cloud account which has a free tier if you are just getting started for all users. I have broken up the products you would use by function.
Try it out
This was a quick overview of deep learning and what Cloud products you can use to solve meaningful environmental challenges like detecting deforestation in extractive supply chains. If you would like to try it out, check out our code sample here (click “open in colab” at the bottom of the screen to view the tutorial in our notebook format or click this shortcut here).
By: Alexandrina Garcia-Verdin (Geo for Environment Developer Advocate)
Source: Google Cloud Blog
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