04-762 Machine Learning for Earth Observation

12 units, Carnegie Mellon University

The course introduces some of the advanced techniques for collecting, processing and analyzing remote sensing data. It also discusses some of the machine learning techniques used for geospatial data analytics, including image understanding (e.g., semantic labeling, object detection, data mining, image retrieval), image processing (e.g., compression, denoising, segmentation, classification), data fusion (e.g., optical-SAR fusion, pan-sharpening), transfer learning, cross-sensor learning, etc. Through a group project, students will demonstrate their ability to apply acquired skills to solve a real-world problem of their choice.

Prerequisites: none listed

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