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AI4EO-Plattformen und Best Practices

ED110156Elective Modules5 ECTSEnglishWintersemester/SommersemesterLehrstuhl für Data Science in Earth Observation (Prof. Zhu)
AI-edited module sheet. Based on the TUMonline module description, edited for readability.Original in TUMonline

What it is about

In this module you will learn how to prepare earth observation data for machine learning tasks, create benchmark datasets, and train ML models for classification and semantic segmentation. You will practice using various computing platforms, debugging convergence issues, and best practices for experiment reproducibility. By the end, you will be able to curate datasets, organize experiments, and disseminate results online.

What you will be able to do

  • download uncontrolled Earth observation data
  • find curated benchmark datasets for downstream tasks
  • use popular geodata ML libraries
  • control, log, and visualize model training runs
  • disseminate results of experiments online

What the module consists of

  • VorlesungIntroduction of new concepts through presentations and demonstrations
  • ÜbungLive coding exercises and extension of demonstrations for practical application

Teaching method

  • PowerPoint-PräsentationenIntroduction and explanation of new concepts
  • Live-CodingDemonstration of practical implementation and examples for follow-along
  • ÜbungsaufgabenDeepening concepts through practical work on various platforms and libraries

Dates

Lecture with exerciseAI4EO Platforms and Best Practices

  • Fri15:00–17:000120, Hörsaal gem. Nutzung GEO,SP,WS,OR (0501.EG.120)
    15× · 16.10.–05.02.
    • 16.10.
    • 23.10.
    • 30.10.
    • 06.11.
    • 13.11.
    • 20.11.
    • 27.11.
    • 04.12.
    • 11.12.
    • 18.12.
    • 08.01.
    • 15.01.
    • 22.01.
    • 29.01.
    • 05.02.

From the current semester, not binding.

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Official page in TUMonline · Details are not binding.