. . "201800273" . "ARS_001" . "7"^^ . "196"^^ . "10"^^ . "2023-02-02T23:00:00Z"^^ . "f2f" . "online" . "The course is designed for self-directed learning in an online (e-learning) setting. Independent of the COVID-19 situation, the majority of the course can be done online or at home. The course uses short lectures to introduce course components; interactive sessions for plenary question-and-answer moments as well as personalized feedback; and individual practical assignments. During the course is ample time for self-study and experimenting with scripting and data processing."@en . . . . . . "Students should have introductory-level experience with GIS and Remote Sensing and possess an affinity with earth sciences, physical geography or spatial sciences.An account with Google for accessing the EarthEngine ,Participants should have introductory-level experience with GIS and Remote Sensing and possess an affinity with earth sciences, physical geography or spatial sciences. Participants will need an account with Google and Google Earth Engine to follow the practicals."@en . . . "3"^^ . "2" . "1B" . . . . "2022-11-13T23:00:00Z"^^ . "Earth observation (EO) satellites generate large amounts of geospatial data that are freely available for society and researchers. Technologies such as cloud computing and distributed systems are modern solutions to access and process big Earth observation data. Examples of online platforms for big Earth observation data management and analysis are, just to name a few popular ones, the Google Earth Engine, the Sentinel Hub and the Open Data Cube.\n\nThis course is on processing remote sensing data from operational and historic missions in an online platform, with specific emphasis on earth science applications. The course first gives an introduction to scripting with a higher-level programming language, such as Python or JavaScript. Writing own scripts allows to create custom processing solutions, automate such processing chains, apply them to various remote sensing data and provide scalable solutions for handling small or large data sets. The application to Earth sciences will help you to recognize landforms in images, determine earth surface composition and derive various physical parameters from the Earth surface."@en . "Spectral Data Processing"@en . . "Spectral Data Processing"@en . "Spectral Data Processing"@en . . . "Learning outcome"@en . . "evaluate the quality of processing results and judge their suitability for further interpretation."@en . "evaluate the quality of processing results and judge their suitability for further interpretation."@en .