Space training opportunities
593 training opportunities for the UK and European space sectors, last updated 29 April 2024. Curated by Space Skills Alliance.
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Found 6 training opportunities
Displaying all 6 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 · 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.
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.
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.
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.
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.
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).
Displaying all 6 opportunities