Foundations of 3D LiDAR Data and Spatial Analysis

3D spatial data is transforming how we map and understand our physical world, yet working with raw point clouds can feel intimidating. This comprehensive written course guides you step-by-step through the core concepts and workflows of LiDAR technology. You will transition from a beginner to a confident analyst capable of importing, filtering, and analyzing 3D point clouds. By studying clear explanations and practical code examples, you will learn how to extract actionable terrain and canopy models for real-world GIS projects. What you'll learn: - Understand the physics of active remote sensing, sensor types, and LiDAR return intensities. - Process raw LAS and LAZ files using modern open-source tools and Python libraries like laspy. - Classify point cloud data to separate ground points from vegetation and human-made structures. - Generate highly accurate Digital Elevation Models (DEMs) and Canopy Height Models (CHMs). - Apply modern spatial analysis workflows to assess environmental changes and urban development. The course begins with essential terminology and the physical principles of laser scanning before progressing to hands-on processing techniques. You will work through structured written exercises and conceptual walkthroughs designed to build your practical spatial analysis skills. This course is designed for GIS professionals, environmental analysts, and beginners looking to expand their spatial data toolkit. No prior experience with LiDAR or programming is required. Start exploring the power of three-dimensional spatial data today.