Space training opportunities
627 training opportunities for the UK and European space sectors, last updated 8 October 2024. Curated by Space Skills Alliance.
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Found 16 training opportunities
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Space * ASTROBIOLOGY
Short course (Strasbourg) by International Space University (ISU) · €2000 (~£1695)
Astrobiology is the interdisciplinary study of life in the universe and touches on some of the most profound questions in both science and philosophy. How and when did life on Earth emerge from a ‘pool of organic molecules’? Is there life on Mars, extant or extinct? Could other bodies in the solar system harbor life, e.g. Enceladus, Titan and Europa? Do intelligent civilizations exist elsewhere in our galaxy? All of these questions – and many more – will be addressed in ISU’s 7th annual Astrobiology Elective (April 11-21, 2023). The Elective will welcome some of the finest Astrobiologists in the world, e.g. Dr. Christopher P. McKay (NASA Ames Research Center), Dr. Frances Westall (Director Emeritus of the CNRS Exobiology Group, Orléans) and Dr. Joseph A. Nuth, III (NASA Goddard Space Flight Center). The program will highlight current and future astrobiology missions, e.g. Martian rovers such as Perseverance and its successors. The Elective will consist of presentations, roundtable discussions, workshops, and a field trip devoted to collecting and analyzing magnetotactic bacteria. The single deliverable will be a group White Paper, of which there will be four offered. In the past, they have included topics such as, “The potential of the Canadian Arctic for Astrobiology research”, and “Artificial Intelligence (AI) as a Biomarker for the Astrobiology Community”.
Earth Observation & Geospatial Analysis: from satellite to end-user
Short course (Online) by Spaceway · Past · €179 (~£152)
This Crash Course has the goal of providing the participants with knowledge in Earth Observation, from satellite to image to information delivery with application to agriculture and biodiversity. You will learn about remote sensing techniques, geospatial analysis, GIS, Big Data, Machine Learning, Artificial Intelligence, and all the potential value of these technologies for Earth applications.
Modeling Forest Aboveground Biomass using EO Data and Machine Learning: Challenges and Opportunities
MOOC (Online) by AI.Geolabs · Free
Miombo woodland ecosystems (in Africa) play a vital role in the global carbon cycle, however, it is currently difficult to know how much carbon they store and sequester due to a lack of data. Therefore, an accurate estimation of forest above ground biomass (AGB) is required to provide the baseline of forest carbon stocks and quantify the anthropogenic emissions caused by deforestation and forest degradation. In addition, accurate estimation of forest AGB is critical to implementing cost-effective carbon emission mitigation strategies.
EO Methods and Data
Resource (Online) by Sentinel Hub · Free
If you’re a beginner in remote sensing or just starting out with Sentinel Hub, check out our short video course on remote sensing essentials with a comprehensive overview of common use cases and tools. This is a great introduction to what you can do with Sentinel Hub, touching on scripting, EO Browser, API, and even machine learning!
Geospatial Machine Learning for Mapping Urban Land Cover in Earth Engine
MOOC (Online) by AI.Geolabs · Free
The purpose of this course is to explore Sentinel-2 and Sentinel-1 image collection in GEE. We will compile quarterly multi-seasonal Sentinel-2 and Sentinel-1 imagery collection scenes acquired between January and October 2020. Quarterly multi-seasonal composite imagery comprises composites for the rainy (January – March), post-rainy (April-June), and dry season (July-October) for Harare, which is going to be the case study.
Deep Learning for Mapping
MOOC (Online) by AI.Geolabs · Free
This course provides guidelines on implementing deep learning-based semantic segmentation to detect or map urban features such as building footprints and roads. We are going to use VHR imagery for mapping the urban elements. This course consists of two labs. Lab 1 will focus on instance segmentation using Mask-RCNN on a local machine (CPU or GPU), while lab two will focus on instance segmentation using Mask-RCNN on a Google Colab.
Data-centric Explainable Machine Learning for Land Cover Mapping
MOOC (Online) by AI.Geolabs · Free
In this course, explainable machine learning refers to the extent to which the underlying mechanism of a machine learning model can be explained (Biecek and Burzykowski 2020). That is, explainable machine learning models allow us (humans) to explain what the model learned and how it made predictions (post-hoc). Note this is different from interpretable machine learning (e.g., linear and logistic regression models), which refers to the extent to which a cause and effect are observed within a model (Molnar 2019).
Spatial Data Science: The New Frontier in Analytics
MOOC (Online) by Esri · Free
Use location to find patterns and tackle complex problems. Spatial data science allows analysts to extract deeper insight from data using a comprehensive set of analytical methods and spatial algorithms, including machine learning and deep learning techniques. This course explores the application of spatial data science to uncover hidden patterns and improve predictive modeling. You'll work with powerful analytical tools in Esri's ArcGIS software and learn how to integrate popular open data science packages into your analyses.
Earth Observation: Disruptive Technology and New Space
MOOC (Online) by European Space Agency (ESA) and Imperative Space · Free
This is a short MOOC from ESA which consists of a series of interviews with leading experts across Earth Observation and related technologies. This MOOC also acts as an additional section for the 'Earth Observation from Space: The Optical View' MOOC. The explosion in Earth Observation (EO) data from the Sentinel programme, a new generation of commercial satellites, and emerging constellations of small-sats, has created one of the greatest ‘big data’ challenges in the world today. In this course you will explore technologies such as AI, 3D data visualisation, cloud computing technologies and blockchain, and learn how they are meeting the needs of the ever growing data analytics and data navigation challenges in EO. This short course now includes an extra module on how ESA is responding to the digital trends highlighted in previous modules, and the real-world impact emerging from ESA's Phi innovation strategy.
Agricultural Crop Classification with Synthetic Aperture Radar and Optical Remote Sensing
Seminar (Online) by European Space Agency (ESA) · Free
This five-part, intermediate webinar series focused on the use of synthetic aperture radar (SAR) from Sentinel-1 and/or optical imagery from Sentinel-2 to map crop types and assessed their biophysical characteristics. The webinar covered a SAR and optical refresher along with pre-processing and analysis of Sentinel-1 and Sentinel-2 data using the Sentinel Application Platform (SNAP) and Python code written in JupyterLab, a web-based interactive development environment for scientific computing and machine learning. The webinar also covered an operational roadmap for mapping crop type, including best practices for collecting field data to train and validate models for classifying crops on a national level. The final session of this series covered crop biophysical variable retrievals using optical data.
Robotics Workshop
Short course (Belgium) by European Space Agency (ESA) · Free
Planetary exploration is a fascinating sector that ESA is investing heavily in, with projects including the pioneering Rosetta mission to a comet, the ambitious Artemis mission to the Moon (constructing the European Service Module), and the cutting-edge Rosalind Franklin rover destined for Mars. The present and future missions of Mars exploration are the inspiration behind the Robotics Workshop. Over four days, participating students will delve into the design and operation of a planetary exploration robotic vehicle based on the European rover that will be sent to investigate the surface of the red planet. Particular attention will be given to software, starting with installing basic components of Linux and ROS (Robot Operating System). Students will then study the locomotion system, developing algorithms that will allow the robot to move. Finally, Machine Learning and Artificial Intelligence will be utilised to grant the capability of recognising specific objects on the surface of Mars.
Artificial Intelligence (AI) for Earth Monitoring
MOOC (Online) by FutureLearn · Free
Explore how artificial intelligence (AI) and machine learning (ML) technologies are helping to advance Earth monitoring.
Deep Learning for Computer Vision
Resource (Online) by NEODAAS · Past · Free
This site contains material used for the NEODAAS & FSF training course held in October 2021.
Commercialising Space Data with AI
Seminar (Goonhilly) by Cornwall Space Cluster · Past · Free
Cornwall Space cluster, Goonhilly Earth Station Ltd and a team of experts will explore how business challenges can be addressed by combining data gathered from spacecraft with innovative AI and Machine Learning techniques.
IV ESA EARSEL CNR School: Remote Sensing for Forest Fires
Workshop (Spain) by European Space Agency (ESA) · Past · Free
Hand on training on of optical and radar sensors for fire hazard estimation, including as Self Organized Maps and Machine Learning
Machine Learning for Earth Observation
Workshop (Truro) by Truro and Penwith College · Past · Free
This 2-day course will enhance your knowledge of Machine Learning in an Earth Observation context, providing you with useful skills for the sector. The course will cover the following topics: Introduction to Remote Sensing and Machine Learning We will ensure all learners have an understanding of Remote Sensing/Earth Observation data, the sources and the broad uses. It will also introduce Machine Learning in the context of RS/EO. Overview of GIS data analysis tools This module is intended to introduce the learners to the broad variety of tools available (open source) for the analysis and interrogation of geospatial data. Remote Sensing Systems This module will introduce the satellite-borne sensors we commonly use, particularly those on open source platforms. We will discuss the data formats and types that those sensors produce. Machine learning for land use and land cover applications. This will be an applied module diving deeper into an application of ML techniques. Machine learning for Object detection and Segmentation. This is a further applied module, which will look at a further application of ML and introduce Deep Learning.
Displaying all 16 opportunities