. . . . "MGEO 5.0 BoK"@en . . "5.0" . "140.0" . "10.0" . . . . . . . . "F2F" . . . . . . . . . . . . . . . . . . "Statistics, calculus, linear algebra, analytics geometry, programming (Python)"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "4.0" . "3.0" . "CONCEPT (not yet confirmed by all GEO-AI staff):\nThis 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.\nFrom 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.\nFeature 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.\nClustering techniques, for exploratory spatial data analysis, will be introduced to help you to discover hidden structures and trends within geospatial datasets.\nClassification 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.\nThroughout 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.\nBy the end of this course, you will be adept at applying machine learning techniques to geospatial sciences."@en . "Machine Learning for Geospatial Sciences"@en . . "Machine Learning for Geospatial Sciences"@en . "Machine Learning for Geospatial Sciences"@en . . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . "Foundation, CORE Book"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "4.0" . "2.0" . "Remote sensing is a unique tool to observe the Earth system, and to quantitatively monitor a variety of key atmospheric, land and ocean variables by measuring radiation reflected or emitted by the earth or atmosphere. With the availability of more and more remote sensing data from various types of instruments with different spectral characteristics, temporal and spatial resolutions, the field of quantitative land remote sensing is advancing rapidly. This course provides an overview of Earth Observation from Space by describing basic concepts of orbits and viewing from space, instrument characteristics as well as exploring the electromagnetic radiation ranges used by remote sensing devices, like in the VIS, NIR, SWIR, TIR atmospheric windows and active and passive Microwave regions, but also within atmospheric absorption bands. Radiative transfer equation and atmospheric correction for signal correction are discussed and practised. \n\nAttention is given to space and ground segments, operational (meteorological) satellite programmes within the ocean and sea ice, land and atmospheric domains and the retrieval of various space based observations of geophysical variables and their availability in cloud repositories and online processing platforms, and their retrieval.\n\nAlso attention is given to calibration and validation, related to instrument calibration (before launch, on board and vicarious calibration) but also to bias adjustment of long term data records and the need of validation when using the geophysical variables obtained through space based observations. "@en . "Quantitative Remote Sensing 5 EC Resource Security"@en . . "Quantitative Remote Sensing 5 EC Resources Security"@en . "Quantitative Remote Sensing 5 EC Resource Security"@en . . . . . . . "Prepare datasets ready for analysis"@en . . . . . . . "Select appropriate datasets to address geospatial related applications using machine learning algorithms"@en . . "dataset"@en . . .