Machine Learning for Preventing Deforestation
Convolutional Neural Networks
“Forests cover 30% of the world’s land area.”
“Deforestation is the 50% loss of the world’s tree count. People cut down 15 billion trees every year. “ Long-term, it worsens climate change.
“Deforestation costs $4.5 trillion each year through the loss of biodiversity and it has eliminated habitat for millions of species. In fact, 80% of Earth’s land animals and plants live in forests.”
Solution: identify deforestation equipment in satellite images and prevent deforestation before it happens.
I have chosen a supervised approach, manually labeling positive and negative classes, and augmenting with the use of Keras.
When it comes to data resources, it can be achieved by the use of:
After training the neural network model, built with 2D Convolution Layers, the following objects were classified with a very good score:
As you can see below, the model has no problem with identifying the needle in the haystack.
For demo purposes, the test images were collected via the internet(as the watermarks makes it clear).
I hope you’ve enjoyed this quick look at what can be possible in the deforestation regards and of course, can be applied to other industries.
