Machine Learning and Deep Learning for Earth Observation
Machine Learning and Deep Learning for Earth Observation
Published 9/2025
Duration: 3h 13m | .MP4 1920x1080 30 fps(r) | AAC, 44100 Hz, 2ch | 1.37 GB
Genre: eLearning | Language: English
Published 9/2025
Duration: 3h 13m | .MP4 1920x1080 30 fps(r) | AAC, 44100 Hz, 2ch | 1.37 GB
Genre: eLearning | Language: English
Essentials of Machine Learning and Deep Learning for Earth Observation
What you'll learn
- Identify key ML and DL concepts, workflows, and Earth Observation applications, including supervised, unsupervised, CNN, RNN, and GAN methods.
- Implement core algorithms such as logistic regression, SVM, k-means, CNNs, and RNNs to classify, detect, and analyze remote sensing imagery.
- Evaluate and optimize models using cost functions, gradient descent, learning-rate schedules, regularization, data augmentation, and transfer learning.
- Build and fine-tune Python-based ML/DL pipelines for remotely sensed data to solve practical Earth Observation problems.
Requirements
- No advanced background is needed. Just basic Python knowledge (e.g., running notebooks and simple functions) and curiosity about Earth Observation. Familiarity with arrays or linear algebra is helpful but not required. Learners need a computer with internet access and the ability to install common Python libraries (NumPy, scikit-learn, TensorFlow or PyTorch). All essential concepts will be explained step by step, making the course accessible to motivated beginners.
Description
Greetings to Colleagues from Every Corner of the World!
Welcome toEssentials of Machine Learning and Deep Learning for Earth Observation- your gateway into the rapidly growing field of AI4EO. This course is designed to give scientists, engineers, researchers, and data enthusiasts a clear and practical foundation in the core ML and DL techniques driving innovation in Earth Observation, while also emphasizing real-world challenges and solutions.
Across three hours of focused content, you’ll move from the basics of machine learning pipelines and classic algorithms to modern deep learning architectures like convolutional, recurrent, and generative networks. You’ll discover how these methods power essential AI4EO applications such as scene classification, semantic segmentation, object detection, and change detection, and learn best practices for effectively preparing and handling geospatial data.
No advanced background is required - just basic Python skills and curiosity. Through concise explanations, Earth Observation examples, and hands-on coding exercises, you’ll build confidence in applying ML and DL to remote-sensing data.
By the end of this course, you’ll have the knowledge and skills to appreciate practical limitations, start building your own AI-powered geospatial solutions, and establish a solid foundation for exploring specialized AI4EO research and applications, including scene classification, semantic segmentation, object detection, and change detection.
Who this course is for:
- This course is perfect for scientists, engineers, students, and professionals eager to apply ML and DL to Earth Observation. Itis a great start for anyone wanting to dive into AI4EO concepts, including scene classification, semantic segmentation, object detection, and change detection. Remote sensing practitioners, data scientists exploring geospatial AI, and beginners with basic Python skills will all find this course an accessible and valuable entry point into AI for Earth Observation.
More Info