Deep learning & Remote sensing analYsis for Agroforesty and Drought (DRYAD)

DRYAD was created to improve the convenience of drought monitoring for both remote sensing experts and non-experts and to expand the potential applications of deep learning in remote sensing data analysis. DRYAD offers robust tools for the analysis of Earth observation data, including satellite imagery and meteorological data in raster (.tif) format. The software enables the swift calculation of complex drought indices and provides deep learning-based semantic segmentation. 

Main algorithms included:

• U-Net segmentation
• Time series NDVI reconstruction
• Outlier removal with Region growing 
• Standardized Precipitation Index(SPI) calculation in tif format
• Standardized Precipitation Evapotranspiration Index(SPEI) calculation in tif format
• Vegetation Temperature Condition Index(VTCI) calculation

You can access detailed information by visiting our platform (https://www.platform-dryad.com/service-program)