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Working in a hands-on learning environment, led by our Monitoring Changes in Surface Water Using Satellite Image Data expert instructor, students will learn about and explore:
Accessing cloud data servers to download satellite imagery.Manually creating your own ground truth data from imagery.Using the VGG-JSON image annotation format.Using Graphical Processing Unit (GPU) computation on Google Colab.Merging imagery and performing operations on raster datasets.Using Keras and TensorFlow for deep learning.Evaluating model performance by comparing estimated and observed results.Data augmentation for boosting model training.Optimizing model performance using experimentation.Understanding model performance metrics (such as Dice and Jaccard scores).

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Last Updated

July 29, 2021

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In this, you’ll fill the shoes of a data scientist at UNESCO (United Nations Educational, Scientific and Cultural Organization). Your job involves assessing long-term changes to freshwater deposits, one of humanity’s most important resources. Recently, two European Space Agency satellites have given you a massive amount of new data in the form of satellite imagery. Your task is to build a deep learning algorithm that can process this data and automatically detect water pixels in the imagery of a region. To accomplish this, you will design, implement, and evaluate a convolutional neural network model for image pixel classification, or image segmentation. Your challenges will include compiling your data, training your model, evaluating its performance, and providing a summary of your findings to your superiors. Throughout, you’ll use the Google Collaboratory (“Colab”) coding environment to access free GPU computer resources and speed up your training times.

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