Machine Learning for Remote Sensing
A Guide to Earth Observation with Satellite Data, AI and Python
Machine Learning for Remote Sensing is your guide into the world of earth observation with artificial intelligence.
Where To Buy
The book isn’t finished yet, but you can check out the in-progress version here:
Summary
Silently, thousands of satellites fly through orbit, observing Earth. Satellite imagery provides a non-intrusive way to observe earth, giving insight into land use, population size estimation, and wildfire prediction. But without the technology to analyze the data and make predictions at scale, it’s just a barrage of beautiful pictures. Fortunately, we have machine learning, especially deep learning, to make sense of the data.
At first glance, satellite images are just images. But satellite images are a very special type of image: They can have much more layers than just red, green, and blue channels. All images are related to each other through space and time. Challenges include clouds, evaluating with correlated data, imbalanced labels, and more.
Who This Book Is For
I write this book from my perspective: It’s for someone with a background in data science, machine learning or statistics background who wants to learn how to model satellite data using machine learning. That’s why I think this book will be useful for:
- People with a basic machine learning background who want go get into remote sensing.
- Machine learning, AI and data science students.
- Researchers who apply machine learning to satellite data.
- Remote sensing experts with a basic understanding of machine learning.