This metadata document provides information about the Soil Productivity Map. This map was generated following the methodology presented in Schaetzl, R. J., Krist Jr, F. J., & Miller, B. A. (2012). A taxonomically based ordinal estimate of soil productivit...
Agriculture productivity maps based on satellite images at the field level are revolutionizing the way farmers manage their crops. These maps provide detailed insights into the health and performance of individual fields, enabling farmers to make data-dri...
Humanitywatch is aimed at assistance actors (humanitarian and development), and institutional actors, all over the world thanks to AI and Earth Observation technologies.
Through using ISR planes, optical, RF and SAR satellite imagery, we achieve persistent anchorage monitoring regardless of cooperability from individual ships.
Through the integration of the Earth Observation data in the usage of AI based applications we could give an insight into what is happening with the environment in fragile and hard to reach areas. The areas of interest here are Ukraine and Mali.
We improve short term solar irradiation forecasts in the 15 minutes to 2 hours range, which is critical for off grid sites or isolated power grids, using AI and DL techniques.
Automatic detection of in-field weeds using super-resolved Sentinel-2 at 1m per pixel imagery and delineated field boundaries.
We broaden the interpretation of intermediate values of NDVI corresponding to the growing stage of a crop with the aim of helping farmers to assess the growth evolution. NVDI extracted from the imagery from the satellite Sentinel-2 for 4 years, for 11 plo...
