. . . . . . . "Develop and apply deep learning algorithms to analyze different spatial and spatio-temporal data types"@en . . . . . . . "Evaluate the performance of the deep learning algorithms, as well as their generalization and transfer capabilities"@en . . . . . . . . "Understand and account for ethical considerations in applying deep learning algorithms in Geoscience use cases"@en . . . . . . . . . . . . "Explain the core principles of deep learning and other advanced machine learning algorithms (e.g., Convolutional NNs, Recurrent NNs, Transformers) and their relevance to different use cases in Geoscience"@en . . . . . . . . . "Explain the basic concepts of explainable AI techniques for interpreting deep learning model results"@en . . . . . . . . . . . . . . . . . . "Research showcases"@en . "Advanced Machine Learning for Geospatial Sciences: LU7"@en . . . . . . . . . . . . . . . . . . "Introduction to Deep Learning"@en . "Advanced Machine Learning for Geospatial Sciences: LU1"@en . . . . . . "Explainable AI"@en . "Advanced Machine Learning for Geospatial Sciences: LU6"@en . . . . . . . . . . . "Transformer networks"@en . "Advanced Machine Learning for Geospatial Sciences: LU4"@en . . . . . . "Assignment"@en . "Advanced Machine Learning for Geospatial Sciences: LU8"@en . . . . . . . . . . "Introduction to Deep Learning"@en . "Advanced Machine Learning for Geospatial Sciences: LU2"@en . . . . . . . "Assessment"@en . "Advanced Machine Learning for Geospatial Sciences: LU9"@en . . . . . . "Assignment"@en . "Advanced Machine Learning for Geospatial Sciences: LU10"@en . . . . . . . . . . . "Advanced Algorithms"@en . "Advanced Machine Learning for Geospatial Sciences: LU5"@en . . . . . . . . . . . "Spatio-Temporal architectures"@en . "Advanced Machine Learning for Geospatial Sciences: LU3"@en . . . . . . . "convolutional neural network"@en . . . . . . . "https://ltb.itc.utwente.nl/page/792/concept/152729" . . "Object detection"@en . . . . . . "https://ltb.itc.utwente.nl/page/792/concept/152732" . . "Remote sensing"@en . . . . . . . . . . . . . . . . . . . . . "advanced deep learning methods"@en . . . . . . "Deep Aggregation Transformer network"@en . . . . . . . "deep learning algorithms application"@en . . . . . . . . . "geospatial science"@en . . . . . . . . "deep learning algorithms generalization"@en . . . . . . "generative networks"@en . . . . . . "advanced algorithms"@en . . . . . . "deep learning research"@en . . . . . . . . . . . . . . . . . . "presentation"@en . . . . . . "GRU network"@en . . . . . . . "foundation models"@en . . . . . . "convolutional operations"@en . . . . . . "semi-supervised learning"@en . . . . . . "object outlines in vector format"@en . . . . . . "deep learning model"@en . . . . . . "ViT"@en . . . . . . "advanced architectures"@en . . . . . . . . "neural network"@en . . . . . . . . . "ML"@en . . . . . . "Data-efficient image Transformers"@en . . . . . . . "different architectures"@en . . . . . . "loss functions optimization algorithms"@en . . . . . . "deep learning model results interpretation"@en . . . . . . "Transformers"@en . . . . . . "deep learning algorithms performance"@en . . . . . . "deep learning algorithms"@en . . . . . . . "Recurrent Neural Networks"@en . . . . . . . . "SDGs"@en . . . . . . . . . . "assignment"@en . . . . . . "time series analysis"@en . . . . . . . "spatial data types"@en . . . . . "explainable AI"@en . . . . . . "deep learning principles"@en . . . . . . "MViT"@en . . . . . . "Fully convolutional networks"@en . . . . . . . "use cases"@en . . . . . . "advanced machine learning algorithms"@en . . . "https://ltb.itc.utwente.nl/page/792/concept/152838" . . "Semantic segmentation"@en . . . . . . . . . "LSTM"@en . . . "https://ltb.itc.utwente.nl/page/792/concept/152842" . . "Earth observation"@en . . . . . . . . . . . "pooling"@en . . . . . . "transfer learning"@en . . . . . . "RNN"@en . . . . . . "geospatial data"@en . . . "https://ltb.itc.utwente.nl/page/792/concept/152853" . . "Machine learning"@en . . . . . . . . . . . . . . . . . "training deep networks"@en . . . . . . . . "AI"@en . . . . . "geoinformatics"@en . . . . . . . . "ethical considerations"@en . . . . . . "test"@en . . . . . . . . "activation functions"@en . . . . . . "spatio-temporal data types"@en . . . . . . . . "EO"@en . . . . . . "deep learning model results"@en . . . . . . "extracting object outlines"@en . . . . . . "format learning activities"@en . . . . . . "transformer networks"@en . . . . . . "fully connected layers"@en . . . . . . "deep learning algorithms capabilities"@en . . . . . . "deep learning methods"@en . . . . . . . "SWIN"@en . . . . . . "explainable AI techniques concepts"@en . . . . . . "regularisation techniques"@en . . . . . . . "instance segmentation"@en . . . "https://ltb.itc.utwente.nl/page/792/concept/152902" . . "Deep learning"@en . . . . . . . . . . . . . . "explainable AI techniques"@en . . . . . . . . . . . . . . . . "Geospatial Information Visualiser"@en . . . . . . . . . . . . . . . "Geospatial Data Engineer"@en . . . . . . . . . . . . . . . "Remote Sensing Specialist"@en . . . . . . . . . . . . . . . "Geospatial Analyst"@en . . . . . . . . . . . . . . . "3D Geoinformation Engineer"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "M-GEO 5.0"@en . . . . . . . . . . . . . . . . . . . . . "GeoAI"@en . . . "Course"@en . "5.0" . "140.0" . "10.0" . "F2F" . "Machine Learning for Geosciences or equivalent"@en . "4.0" . "4.0" . "CONCEPT (not yet confirmed by all GEO-AI staff):\nBuilding on the foundations laid in the Machine Learning for Geospatial Sciences course, this advanced program delves into the forefront of machine learning and deep learning technologies tailored for geospatial applications. Here, you will explore sophisticated models and techniques that enable the analysis of both spatial and spatio-temporal data, addressing complex real-world challenges with precision and insight.\nThe course begins with an in-depth exploration of advanced machine learning algorithms, designed to handle the unique complexities of geospatial data. You will learn to apply these algorithms to model intricate spatial patterns and relationships, enhancing your ability to derive meaningful insights from diverse geospatial datasets.\nDeep learning algorithms, known for their capacity to process large and complex datasets, will be a significant focus. You will master techniques for analyzing geospatial imagery, recognizing patterns, and making accurate predictions. Algorithms like Recurrent NN, Transformers and Graph NN empower you to extract detailed and valuable information from high-resolution and divers geospatial data.\nHandling spatio-temporal data requires specialized approaches. You will learn advanced methods for analyzing time series data, predicting temporal changes in geospatial phenomena, and help understanding the dynamics of processes such as weather patterns, urban growth, and environmental shifts.\nState-of-the-art architectures and methods will be introduced, highlighting their remarkable ability to capture and model complex dependencies in data. You will explore their application in geospatial sciences, particularly in tasks requiring attention mechanisms to focus on relevant spatial regions or temporal sequences.\nAn important component of this course is Explainable AI (XAI), ensuring transparency and interpretability of your models. You will learn techniques to make complex models understandable, fostering trust and facilitating informed decision-making in geospatial applications. We will discuss the ethical implications of AI in geospatial sciences, emphasizing the importance of responsible data use, privacy concerns, and the societal impact of AI-driven decisions.\nBy the end of this course, you will be proficient in leveraging advanced machine learning and deep learning techniques for geospatial sciences, equipped to tackle sophisticated spatial and spatio-temporal challenges ethically and transparently. Join us to advance your expertise and contribute to the transformative power of AI in geospatial sciences!"@en . "Advanced Machine Learning for Geospatial Sciences"@en . "Advanced Machine Learning for Geospatial Sciences"@en . "Advanced Machine Learning for Geospatial Sciences"@en .