Explore LiDAR in 3D is a beginner-level course designed for analysts and GIS professionals who want to work confidently with LiDAR point cloud data for elevation modeling and environmental applications. LiDAR data is inherently three-dimensional, yet many errors in analysis occur when users treat it like traditional 2D raster data. This course focuses on building correct mental models and practical skills for working with LiDAR from visualization through validation.

Across three structured lessons, learners progress from understanding and exploring LiDAR point clouds in a 3D viewer, to generating a Digital Elevation Model (DEM) from ground-class points, and finally to evaluating vertical accuracy using control points. Rather than emphasizing software clicks, the course emphasizes why each step matters, helping learners make sound decisions that support real-world use cases such as flood modelling. Through short videos, targeted readings, Coach-led reflections, and hands-on labs, learners practice loading and interpreting point clouds, creating terrain surfaces correctly, and assessing whether elevation outputs are fit for purpose. By the end of the course, learners will be able to confidently visualize LiDAR data in 3D, generate a DEM from ground points, and evaluate its vertical accuracy—skills essential for delivering reliable elevation models in environmental and risk-analysis workflows.

This course equips learners with practical, job-ready skills to train and evaluate supervised machine learning models for land-cover classification. Learners progress through an end-to-end analytical workflow, beginning with spectral and texture feature engineering, followed by training a Random Forest classifier, and concluding with rigorous validation using confusion-matrix-based accuracy assessment. By the end of the course, learners produce a land-cover map that meets a minimum accuracy threshold, mirroring real-world data analysis workflows.

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.

Harness Earth Engine is a beginner-level course designed for learners who want to explore environmental change using satellite data without managing complex data pipelines. Across a series of focused, hands-on lessons, this course introduces Google Earth Engine as a practical tool for analyzing vegetation patterns at scale. Learners begin by understanding NDVI and its role in environmental monitoring, then progress to working directly with the MODIS NDVI dataset inside Earth Engine.

Through guided videos, readings, Coach dialogues, and hands-on practice, learners build skills to load satellite image collections, reduce time-series data into annual summaries, and visualize long-term vegetation trends using charts. The course emphasizes real-world application, preparing learners to create NDVI trend charts suitable for environmental or sustainability reports. By the end of the course, learners will have a clear, repeatable workflow for transforming raw satellite imagery into meaningful, report-ready insights.