Office for Outer Space Affairs
UN-SPIDER Knowledge Portal
Help Shape the Future of the UN-SPIDER Knowledge Portal
The UN-SPIDER Knowledge Portal is your one-stop platform for resources on space-based disaster risk management. It provides: • Links and guidance on satellite data sources and applications • Recommended practices and step-by-step methodologies • Training materials and tutorials • Case studies and user stories from real-world applications • News and updates on UN-SPIDER activities, events, and global developments
Since its last major review in 2012, the Portal has evolved significantly. Now, we want to hear from you.
We invite you to take part in the 2025 evaluation of the UN-SPIDER Knowledge Portal!
Throughout this course, you’ll embark on an enlightening journey into the realm of Machine Learning (ML) as applied to Earth Observation (EO). We’ll start by exploring the current landscape of ML for EO, shedding light on the latest advancements and addressing pertinent ethical considerations.
You will gain essential Machine Learning (ML) skills tailored for Earth Observation (EO) research, enhancing your proficiency in ML-driven research specifically within the EO domain. Additionally, you will acquire specialized knowledge through a course designed for computer science professionals, enticing you to explore Earth Observation as a compelling field for applied Artificial Intelligence (AI) solutions.
This course targets scientists possessing backgrounds in computer science, to entice them to explore EO as a compelling field for applied Artificial Intelligence (AI) solutions.
Whether you’re a student eager to expand your knowledge, a seasoned professional looking to stay ahead of the curve, or simply curious about the intersection of technology and environmental science, this course is tailor-made for you. Regardless of your background, our aim is to provide valuable background information, foster a deeper understanding of ML principles, and showcase real-world applications relevant to your interests and expertise.
A basic background in both Machine Learning and Earth Observation is of course advantageous.
