Land Use and Land Cover Mapping with Machine Learning in ArcGIS

Accurately mapping geographic features is essential for environmental monitoring, urban planning, and resource management. Traditional manual digitizing is slow, but combining machine learning with spatial data allows you to automate land cover classification with high precision. This text-based course provides a clear, step-by-step pathway to mastering machine learning workflows within ArcGIS. You will transition from understanding basic remote sensing concepts to confidently executing classification models on real-world satellite imagery. What you'll learn: - Understand foundational remote sensing concepts, electromagnetic spectrum bands, and spatial data preprocessing. - Apply pixel-based classification algorithms, including Random Forest and Support Vector Machines. - Execute object-based image analysis using segmentation techniques to group similar pixels. - Configure training samples and manage signature files for supervised machine learning models. - Evaluate classification accuracy using confusion matrices and modern validation metrics. - Map land use and land cover changes using structured, step-by-step GIS workflows. The course starts with essential definitions of remote sensing and image bands before guiding you through training site selection, algorithm configuration, and post-classification cleanup. You will study detailed written walkthroughs and conceptual breakdowns designed to build practical spatial analysis skills. This course is designed for beginner GIS analysts, environmental researchers, and students who want to introduce machine learning into their spatial workflows. No prior machine learning experience is required, though a basic familiarity with GIS concepts is helpful. Start reading today to unlock the power of automated land cover mapping in your spatial projects.