Space training opportunities

596 training opportunities for the UK and European space sectors, last updated 25 March 2024. Curated by the Space Skills Alliance.

Found 6 training opportunities

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Modeling Forest Aboveground Biomass using EO Data and Machine Learning: Challenges and Opportunities

MOOC (Online) by AI.Geolabs · Free · Current

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.

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  • Land
  • Artificial intelligence & machine learning
  • Remote sensing

Vegetation and Forest Cover Applications

MOOC (Online) by AI.Geolabs · Free · Current

Vegetation is essential in the global terrestrial ecosystem since it provides ecosystem services such as protecting the land surface, modifying the local climate, and conserving biodiversity. The structure of vegetation substantially influences ecosystem function and productivity. Scientists report that vegetation structure correlates with biophysical parameters such as aboveground biomass (AGB) and primary productivity. Therefore, detailed quantification of vegetation structure is crucial for assessing the structure and functioning of ecosystems.

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  • Land
  • Remote sensing

Leveraging Earth Observation (EO) Data and Cloud Computing for Disaster Risk Management

MOOC (Online) by AI.Geolabs · Free · Current

The UN report that extreme weather and climate events are projected to increase over decades. Scientists warn that the frequency of cyclones and heavy storms, on the one hand, and the intensity of droughts and wildfires, on the other hand, will likely increase globally. Furthermore, the power of weather- and climate-related disasters will also increase economic losses that affect the poor and marginalized communities. Therefore, there is a need to understand the spatial and inter-annual variability of natural disasters and the population’s exposure to disaster risk. For example, information on the spatial extent of an event and the degree of destruction is critical in supporting emergency response, recovery, rehabilitation, and reconstruction.

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  • Emergency
  • Remote sensing

Geospatial Machine Learning for Mapping Urban Land Cover in Earth Engine

MOOC (Online) by AI.Geolabs · Free · Current

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.

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  • Land
  • Artificial intelligence & machine learning
  • Remote sensing

Deep Learning for Mapping

MOOC (Online) by AI.Geolabs · Free · Current

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.

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  • Artificial intelligence & machine learning
  • Data visualisation
  • Remote sensing

Data-centric Explainable Machine Learning for Land Cover Mapping

MOOC (Online) by AI.Geolabs · Free · Current

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).

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  • Land
  • Artificial intelligence & machine learning
  • Data visualisation
  • Remote sensing

Displaying all 6 opportunities