Earth Engine + TensorFlow

Abhishek Potnis, Ph.D. Student, IIT Bombay

abhishekvpotnis@iitb.ac.in

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Slides: https://bit.ly/2kv1pTx

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Earth Engine + TensorFlow

Abhishek Potnis, Ph.D. Student, IIT Bombay

abhishekvpotnis@iitb.ac.in

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Chris’ Slides:https://goo.gl/Xtr9rk

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What to Expect

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1. Broad overview of Machine Learning in the context of Remote Sensing.
2. Hands-on Exercise applying ML to classify regions of the earth’s surface.
3. Whats next?

Machine Learning (Supervised Learning)

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An algorithm design technique that supplants the “how” with examples of the “what.”

Neural Nets

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• When built up of more than ~2 layers, referred to as a “Deep Neural Net” (or DNN) and training these is sometimes referred to as “Deep Learning”
• A network of activation functions very apt at transforming complex input manifolds.
• We call values at the input “Features,” and values at the output “Labels”.
• https://developers.google.com/machine-learning/crash-course/ to learn more and build intuition.

"Deep learning is a particular kind of machine learning that achieves great power and flexibility by representing the world as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones."

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Ian Goodfellow and Yoshua Bengio and Aaron Courville, "Deep Learning"

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Machine Learning in Remote Sensing

Machine Learning in Remote Sensing

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• Predicting climate
• Classifying land-cover
• Classifying land-use
• Removing clouds from images
• Predicting crop yield
• Use your imagination!

ML Input Features for Remote Sensing

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• Per-pixel spectra.
• Time-series.
• Statistics
• Any combination of the above!

TensorFlow

TensorFlow™ is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

Useful docs

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• https://www.tensorflow.org/get_started/
• https://github.com/tensorflow/tensorflow
• https://cloud.google.com/ml-engine/docs/tensorflow/getting-started-training-prediction

Train/Test data (Export.table)

.TFRecord

Image data (Export.Image)

.TFRecord

.train()

.predict()

Predictions

.TFRecord

upload

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https://colab.research.google.com/notebooks/welcome.ipynb

's

Search

Let’s get to work!

1. Get these slides
2. Make a copy of this notebook.
1. (File > Save a copy in Drive)

Where do I go from here?

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• Want to learn more about machine learning? https://developers.google.com/machine-learning/crash-course/
• Need Earth Engine help? https://developers.google.com/earth-engine/help

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Thank You!

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Abhishek Potnis

http://home.iitb.ac.in/~abhishekvpotnis/

abhishekvpotnis@iitb.ac.in