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 4 training opportunities
Displaying all 4 opportunities · Download results (CSV) · RSS feed for this search
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.
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).
Displaying all 4 opportunities