EOSAT 4 SUSTAINABLE AMAZON
Prime company: SARVISION BV (NL)EOSAT 4 Sustainable Amazon demonstrates near real time monitoring of forest disturbances in the Colombian Amazon to support the country in reaching its sustainable development goals.
EOSAT 4 Sustainable Amazon demonstrates near real time monitoring of forest disturbances in the Colombian Amazon to support the country in reaching its sustainable development goals.
The world’s forests have undergone substantial changes in the last decades. Deforestation and forest degradation in particular, contribute greatly to these changes. In certain regions and countries, the changes have been more rapid, which is the case in the Greater Mekong sub-region recognized as deforestation hotspot. Effective tools are thus urgently needed to survey Illegal …
The project aims at determining changes in agricultural management patterns, particularly in crop harvest dates, as accurately as possible using Sentinel-1 radar data, and assessing whether linked to Covid-19.
Atmospheric aerosol particles strongly influence climate by scattering and absorbing light (direct forcing) and by changing cloud properties (indirect forcing). The corresponding radiative forcing represents one of the most uncertain radiative forcing terms as reported by the Intergovernmental Panel on Climate Change (IPCC). To improve our understanding of the effect of aerosols on climate and …
Stratospheric aerosols impact the radiative forcing and thus the energy balance of the Earth’s atmosphere, therefore information about their distribution and variability is of high importance for climate related studies. The main scientific objective of the project CREST is creating a new merged long-term time series of the vertically resolved aerosol extinction coefficients using data …
In a world where public health threats are increasingly challenging, wide availability of EO data, increased computational power and AI untapped potential open new opportunities to create early warning systems and support public authorities in decision making. The AIDEO project explored the use of AI applied to a pool of EO datasets in the context …
The project will develop methodology for grazing detection based on Sentinel 1 and 2 data. address grazing intensity, set out benchmarks for detection units (LU/ha) and test the methodology with selected paying agencies in Europe (Czech, Spanish, Estonian, Swedish).
The EASEQC project aimed at expanding the use of AI/ML for quality control of EO products. The traditional approach to quality control, usually involving deterministic models together with considerable manual intervention, is no longer feasible given increasing data volumes of EO data archives. ML/AI has potential to make the process of quality control more efficient. …