Deep Learn Imagery

Overview

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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.

Syllabus

Fine-Tuning CNNs for Land Cover

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.

Improving Model Performance with Data Augmentation

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.

Explaining Model Predictions with Grad-CAM

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.