CONCEPT (not yet confirmed by all GEO-AI staff):
This course is designed to guide you through the intersection of machine learning and geospatial sciences, providing you with the expertise to address pressing societal and environmental challenges. You will be introduced to the foundations of supervised and unsupervised learning algorithms, exploring their applications in the geospatial domain. You will learn popular learning algorithms to address various inference tasks, such as clustering, regression and classification.
From satellite imagery to GIS datasets you'll master the tools and methodologies required to preprocess, analyze, integrate and visualize them. You will gain the skills needed to extract meaningful patterns and insights from these geospatial datasets.
Feature extraction and engineering are critical steps in building effective machine learning models. You will explore techniques to transform raw geospatial data into relevant features enabling your models to learn and predict more effectively.
Clustering techniques, for exploratory spatial data analysis, will be introduced to help you to discover hidden structures and trends within geospatial datasets.
Classification and regression methods like decision trees, random forests, support vector machines and neural networks are pivotal machine learning tasks that you'll apply to a wide array of geospatial problems. Whether it's land use classification, predicting environmental changes, or estimating spatial variables like temperature or population density, you'll develop models that provide precise and actionable insights.
Throughout the course real-world case studies will demonstrate the transformative impact of machine learning on geospatial sciences. You'll work on projects that tackle contemporary issues such as urban planning, environmental monitoring, and disaster management.
By the end of this course, you will be adept at applying machine learning techniques to geospatial sciences.