A forest is a dynamic and complex ecosystem. Dynamic – because it changes over time, complex – because we are talking about its vertical and horizontal structure. Proper forest management and protection requires detailed information, preferably information about the species/species groups of the trees. Information about the species, and additionally e.g. age/height of the trees is a set of information that can tell specialists a lot. On this basis, it is possible, for example, to determine the extent of ecosystem services that a particular forest can provide, it can say something about the size of the tree population or allow conclusions to be drawn about the stability of the forest.

CloudFerro together with the QED Software team is working on a Research & Development project AgroTech to test and review its cloud capabilities for agriculture-related applications. After 1 year of collaboration on the project, we would like to share our main thoughts about implementing solutions for agriculture on CREODIAS.

In order to talk about remote sensing analyses of the forest, we must first define what a forest is. We encounter the first problem here as the definition of the forest is not uniform. There are more than two hundred definitions of this word worldwide, which, of course, complicates the matter as the spatial scale of the analyses increases, and at the same time, it means that remote sensing data can sometimes be used in a limited way. 

Machine Learning presents an attractive opportunity for supplementing or substituting traditional methods of data processing, including Earth Observation data. Training and tuning ML models are most efficient with a quick feedback loop on the training results. That is where GPUs show their great performance advantage over running the training on regular CPUs.

Spatial transformations and an increased share of imperviousness surfaces make cities the places with the most intense impact on the microclimate. The temperature difference can be as high as 10 degrees Celsius compared to undeveloped rural areas. The CREODIAS platform provides data and all necessary tools to better explore spatial relations that help map and prevent the negative effects of the urban heat island phenomenon.

Wildfires are one of the greatest threats to the ecosystems and the potential losses cover all its elements: wildfires affect soil cover, vegetation, fauna mortality, and the atmosphere, releasing greenhouse gases that affect the composition and its functioning, but also wildfires are a threat to human life and the economy, especially in places where the occurrence of wildfires is not a typical phenomenon. 

The availability of Earth observation data supports research conducted by scientists from around the world. In that matter, access to historical EO data is particularly invaluable. It provides better knowledge and understanding of the processes affecting our planet, not mentioning the significant impact it has on the direction of the downstream space technology market. Considering the vast and constantly growing amount of data it is crucial to utilize technologies that make researching and analyzing these data faster. This is where the ESA’s CREODIAS platform comes to play.

Atmosphere monitoring and weather forecasting have always been an important area of research, but nowadays it is attracting growing attention largely due to the availability of advanced technologies. An increasing amount of data, easy access to computing power and scalable cloud solutions with Interactive Development Environment (like Jupyter Notebook) make complex analysis more accessible and lower entry barriers

Which software for checking farmers declaration will fit your needs? What kind of properties does it need? Read the article below to learn why cloud infrastructure meets all the requirements for running CAP software.