GMFM350 Big data and machine learning in remote sensing

Lectures: Characteristics and processing chains of remote sensing data. Big data concepts and tools in remote sensing. Machine learning (ML) and deep learning (DL) methods and algorithms used in remote sensing. ML and DL in remote sensing image classification, segmentation and processing (correction and enhancement). ML and DL methods for analyzing higher dimensional remote sensing data such as laser scanning data, image timeseries, and hyperspectral images.

Exercises:1: Remote sensing big data computation: image collections, data cube, and image timeseries. 2: Satellite image classification and segmentation using Machine learning and deep learning methods. 3: Use of machine learning and deep learning in high dimensional remote sensing data. Python is the workhorse of this course and basic competence in Python is required.

Knowledge: After completing the course, the students will have obtained substantial insightin machine learning and deep learning algorithms used in the processing and analysis of various types of remote sensing images particularly in image preprocessing, classification, segmentation, and change detection. Additionally, the students will have been acquainted with the understanding the concepts, characteristics, challenges and opportunities of remote sensing big data. The students will be able to connect real world remote sensing related problems with machine (deep) learning methods and big data tools.

Skills: The students will be able to manage and compute remote sensing big data with appropriate tools. They will also be able to solve selected remote sensing problems using machine learning and deep learning methods. They will be able to design, plan, train and deploy state-of-the-art machine learning and deep learning methods to solve selected remote sensing problems.

General Competence: They familiarize themselves with the working mechanisms of machine learning, a part of AI, in real world problems that remote sensing is expected to solve. They familiarize themselves with AI, that is widely expanding in the job market. The students will enhance their skills in collaborative works. They will also enhance their skills in report writing and scientific communication.