Advanced Ai For Earth Observation
Published 9/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 4h 52m | Size: 2.34 GB
Computer Vision Techniques for Scene Analysis, Object Detection, and Change Monitoring in Remote Sensing
What you'll learn
Understand key AI tasks in remote sensing: classification, detection, segmentation, and change detection.
Explain how deep learning models work for analyzing satellite and aerial imagery across core remote sensing tasks.
Evaluate models using metrics like precision, recall, F1 score, IoU, and overall accuracy.
Explore real-world applications in urban growth, disaster response, environment, and land-use monitoring.
Apply theoretical knowledge to real-world scenarios by engaging in hands-on practice with practical examples.
Requirements
While no specific background is required, a basic understanding of remote sensing or Earth observation is helpful. Familiarity with core machine learning or deep learning concepts is recommended. Since the course includes hands-on practice, some experience with Python will be valuable.
If you'd like to dive deeper into the foundations of Machine Learning and Deep Learning for Earth Observation - including core concepts, algorithms, and hands-on coding exercises - please see our other course: Machine Learning and Deep Learning for Earth Observation . It provides the solid background needed to get the most out of this advanced course.
Description
Welcome to AI for Remote Sensing: From Theory to Practice Ever wondered how satellites and AI team up to understand our planet? In this course, we'll explore five of the most important computer vision tasks for Earth Observation: scene classification, semantic segmentation, object detection, change detection, and anomaly detection.We'll start with the theory - the core concepts, model architectures, and evaluation metrics you need to understand how AI "sees" the Earth from above. From CNNs and U-Nets to Siamese networks and domain adaptation, you'll get a strong conceptual foundation that prepares you for both academic research and applied projects.But this isn't just theory! The course includes hands-on practice with real datasets, plus code demonstrations that guide you step by step. You'll learn not only how these methods work, but also how to implement and experiment with them in practice to solve meaningful problems.By the end, you'll be able to confidently connect cutting-edge AI methods with real-world Earth Observation challenges - from urban growth monitoring to disaster assessment and environmental sustainability. You'll gain practical experience, sharpen your theoretical knowledge, and build confidence in applying machine learning and deep learning to remote sensing.So whether you're a student, researcher, or professional curious about AI for geospatial intelligence, this course gives you the knowledge and the tools to dive right in.
Who this course is for
Remote sensing and Earth observation professionals eager to master scene classification, semantic segmentation, object detection, change detection, and anomaly detection.
Curious learners who may not have prior EO experience but are excited to explore how AI detects patterns, objects, and changes on our planet.
Students and researchers in geoscience, computer vision, or data science who want a strong theoretical and practical foundation in AI for EO.
Practitioners looking to enhance their skills through hands-on exercises with satellite and aerial imagery.
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