Object-based Image Analysis & Classification in QGIS ArcGIS

Course Highlights:

Discover the Power of Object-Based Land Use and Land Cover Mapping with Machine Learning and Remote Sensing Data in QGIS and ArcGIS

This comprehensive course is tailored for individuals familiar with QGIS and ArcGIS basics, seeking to advance their geospatial analysis skills. Dive into sophisticated geospatial analysis techniques, including segmentation and object-based image analysis (OBIA) for land use and land cover (LULC) mapping, all while applying cutting-edge Machine Learning algorithms. Elevate your expertise in QGIS, ArcGIS, and satellite-based image analysis to master one of the most sought-after tasks in Remote Sensing: land use and land cover mapping.

Course Introduction:

Welcome to this intermediate to advanced course on object-based image analysis for land use and land cover (LULC) mapping. Designed to equip you with practical knowledge in advanced LULC mapping, a crucial skill for Geographic Information Systems (GIS) and Remote Sensing analysts, this course empowers you to confidently perform Machine Learning algorithms for LULC mapping, grasp object-based image analysis, and understand the basics of segmentation. All of this will be executed on real data within two of the most popular GIS software platforms: ArcGIS and QGIS.

Prerequisite Knowledge:

Please note that this course is best suited for individuals with basic knowledge of Remote Sensing image analysis.

Unique Approach:

This course distinguishes itself from other training resources through its hands-on, easy-to-follow approach. Each lecture aims to enhance your GIS and Remote Sensing skills, providing practical solutions. You'll gain the capability to analyze spatial data for your own projects, earning recognition from future employers for your advanced GIS skills and mastery of cutting-edge LULC techniques.

Course Content:

Throughout the course, you'll explore the theory behind OBIA and LULC mapping and gain fundamental insights into working with satellite images. You'll discover how to perform image segmentation in QGIS and ArcGIS, mastering all stages of object-based LULC mapping. Additionally, you'll apply OBIA to a real-life object-based crop classification task using actual project data. All image classification processes will leverage state-of-the-art Machine Learning algorithms, including Random Forest and Support Vector Machines.

Target Audience:

This course caters to professionals in various fields, including geographers, programmers, social scientists, geologists, GIS and Remote Sensing experts, and others who require LULC maps and aim to grasp the fundamentals of LULC and change detection in GIS. If you're planning to undertake tasks that demand the use of cutting-edge classification algorithms to create land cover and land use maps, this course will equip you with the confidence and skills to tackle such geospatial challenges.

Practical Exercises:

Engage in practical exercises featuring precise instructions, code snippets, and datasets to create LULC maps and change maps using ArcGIS and QGIS.

Course Inclusions:

Enroll in this course today to gain access to course data, Java code files, and future resources, ensuring a comprehensive learning experience. Don't miss out on this opportunity to expand your geospatial analysis skills.