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Deep Learn Imagery

This course is part of Satellite Imagery, Remote Sensing & Machine Learning Specialization

Instructor: Professionals from the Industry

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Satellite Imagery, Remote Sensing & Machine Learning Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

This hands-on course proves that deep learning isn't just about pressing "run" on a model. It's about turning satellite imagery into actual, useful insights. You'll work with convolutional neural networks for land cover classification, fine-tune a pre-trained CNN using transfer learning, use data augmentation to improve performance, and apply Grad-CAM to see where the model is actually looking. Along the way, you'll practice translating raw satellite imagery into insights you can clearly communicate to others. You are required to have basic Python programming, familiarity with machine learning concepts, and introductory knowledge of neural networks and image data. Designed for beginners in machine learning and remote sensing, Deep Learn Imagery builds your confidence in both working with deep learning and explaining what your models are doing.

Module details

In this module, you will apply transfer learning techniques to fine-tune a pre-trained convolutional neural network (CNN) for land cover classification using satellite imagery. The module focuses on adapting existing vision models to geospatial data under real-world constraints such as limited labeled samples, class imbalance, and spatial generalization challenges.

What's included

2 videos2 readings2 assignments

2 videosTotal 10 minutes

Introduction and Welcome4 minutes
Fine-Tuning Pre-Trained CNNs and Feature Reuse for Satellite Imagery 7 minutes

2 readingsTotal 11 minutes

Avoiding Spatial Data Leakage in Land-Cover Classification Models 8 minutes
Walkthrough - Fine-Tune a CNN for Land Cover Classification 3 minutes

2 assignmentsTotal 25 minutes

Hands-On Learning: Fine-Tune a CNN for Land Cover Classification15 minutes
Practice Quiz: CNN Fine-Tuning Decisions10 minutes

In this module, learners design and apply data augmentation pipelines to improve the generalization of convolutional neural networks trained on satellite imagery. The module focuses on selecting realistic augmentations that preserve spatial meaning while addressing limited and imbalanced land-cover data.

What's included

2 videos2 readings1 assignment

2 videosTotal 8 minutes

Data Augmentation for Land-Cover Classification5 minutes
Building an Augmentation Pipeline for CNN Training4 minutes

2 readingsTotal 10 minutes

When Data Augmentation Hurts Model Performance8 minutes
Walkthrough - Implement and Evaluate a Data Augmentation Pipeline2 minutes

1 assignmentTotal 15 minutes

Hands-On Learning: Implement and Evaluate a Data Augmentation Pipeline15 minutes

In this module, learners use Grad-CAM visualizations to interpret convolutional neural network predictions for satellite imagery. The module emphasizes understanding model attention, identifying failure modes, and communicating model behavior clearly to technical and non-technical stakeholders.