The EO College is a hub for digital learning content regarding Earth observation, remote sensing and related topics. The platform is designed as a repository for open educational resources and online courses.
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.
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.
The UN-SPIDER Beijing Office and China's Ministry of Emergency Management (MEM) will organize the "The United Nations International Conference on Space-based Technologies for Disaster Risk Reduction - Early Warnings for All", scheduled to take place in Beijing later this year in November.
The CogniSAT-6 satellite, developed by Ubotica, has successfully used artificial intelligence (AI) to autonomously detect 37 ships entering the Galveston channel. This marks a major advancement in satellite technology, particularly for disaster risk management and emergency response.
This is event is available for participation on an ongoing basis
Synthetic Aperture Radar (SAR) signals can “see” the surface of the Earth during the day or night, and under nearly all weather conditions. In addition, the signal can penetrate through the vegetation canopy and detect inundation. These capabilities are unique to radar and make it an ideal sensor for flood detection and monitoring.
AmeriGEO participants, disaster management agencies, including domestic and international government agencies, aid organizations, indigenous communities, students, and academics.
