Fundamentals of GeoAI: Deep Learning for Geospatial Analysis

Build U-Net models in PyTorch for satellite imagery, crop mapping, change detection, building segmentation and LiDAR

What you’ll learn
✓ Build and train U-Net deep learning models in PyTorch for pixel-wise segmentation of satellite and aerial imagery from scratch.
✓ Apply GeoAI to real-world use cases: crop mapping, temporal change detection, building segmentation, and LiDAR urban analysis.
✓ Download and preprocess real satellite data from AWS Sentinel-2 and government LiDAR sources for deep learning pipelines.
✓ Evaluate spatial deep learning models correctly using geographic train/test splits to prevent data leakage and ensure real-world generalization.
✓ Create interactive geospatial maps with Folium to visualize and compare deep learning predictions across crop fields, buildings, and urban areas.

Requirements
● Basic Python programming experience — loops, functions, and working with libraries like NumPy and Matplotlib.
● Familiarity with geospatial raster data concepts (what pixels, bands, and coordinate systems are).
● A computer with Anaconda installed or the ability to set up a Python environment — setup instructions are provided in the course.
● No prior deep learning or PyTorch experience needed — neural network foundations are built from scratch in Module 1.

Description
Whether you work in GIS, remote sensing, environmental science, or data science, deep learning is rapidly transforming how we analyze the world from above. This course gives you the practical foundation to work with GeoAI confidently — building real models on real data, from scratch.

Across five hands-on modules, you will tackle the most important use cases in geospatial deep learning today: crop mapping from Sentinel-2 satellite imagery, temporal change detection using a Siamese U-Net, building segmentation from ultra-high-resolution aerial imagery, and multi-class urban segmentation from LiDAR elevation data.

Every dataset in this course is real and freely available. Sentinel-2 imagery is downloaded directly from the AWS Earth Search STAC catalog. Aerial imagery comes from a Dutch government geoportal at 7.5cm resolution. LiDAR tiles are sourced from the Scottish Government open data portal. No synthetic data, no toy examples.

Every model is built from scratch in PyTorch. You will implement single convolutional filters, build encoder and decoder blocks step by step, assemble complete U-Net architectures, and train them on genuine geospatial problems. The course also covers a Siamese U-Net — a specialized architecture designed specifically for change detection that processes two images simultaneously.

A key methodological focus throughout is doing things correctly. Every module uses proper spatial train/test splits to prevent data leakage, ensuring models are evaluated on geographically distinct areas they have never seen. This is how professional geospatial deep learning is done in the real world — and it is what separates this course from generic image segmentation tutorials.

By the end of this course you will have

• Built and trained U-Net models in PyTorch for pixel-wise segmentation

• Processed real satellite, aerial, and LiDAR data end to end

• Implemented spatial train/test splits for honest model evaluation

• Created interactive Folium maps to visualize and compare model predictions

• Applied deep learning to crop mapping, change detection, building extraction, and urban classification

No prior deep learning or PyTorch experience is required. Module 1 builds the complete foundation from first principles before applying it to increasingly complex geospatial problems across the remaining modules. Basic Python experience and familiarity with geospatial raster data concepts are recommended.

This is the GeoAI course built for people who want to do real work — not just understand the theory.

Who this course is for
■ GIS professionals and geospatial analysts who want to move beyond traditional analysis into deep learning and AI-powered spatial workflows.
■ Data scientists and machine learning practitioners who want to apply their skills to satellite imagery, aerial data, and real-world geospatial problems.
■ Remote sensing specialists and earth observation researchers looking to modernize their workflows with PyTorch and neural network architectures.
■ Students and academics in geography, environmental science, or urban planning who want hands-on AI skills applicable to real spatial datasets.

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