MULESME implements a multi-temporal algorithm that uses time series of Sentinel-1 data and ancillary data, such as a plant water content map, as inputs.
Qualitative data such as the plant growth habit (GH), pod constriction (PC) and beak (PB) were first transformed to quantitative data using the ratio of the frequency of a phenotypic class by the total number of observations.
These maps reveal data such as landmarks, airports, assembly plants, bus stops, fiber optics, golf courses, schools, and much more.
It can be applied to any pathogenic proteome data, such as microbial pathogen data of plants and other organisms.
The spatial data sets incorporated into the portal include basic infrastructure data such as roads and electric power plants, potential contaminant-release sources including Superfund and Toxics Release Inventory (TRI) sites, hurricane flooding data, Census data, physiographic data, and remote sensing imagery both pre- and post-Katrina.
Plant operation data, such as flow, temperature, pH, conductivity and the pressure of SWRO feed, product and reject were collected on a daily basis.
RAPID-N features an innovative data estimation framework to complete missing input data, such as on-site natural hazard parameters and plant unit characteristics.
The spatial data sets incorporated into the portal contain basic infrastructure data such as those on roads and electric power plants, potential contaminant sources including Superfund and Toxic Release Inventory sites, hurricane flooding data, Census data, physiographic data, and remote sensing imagery both pre- and post-Katrina.
Some agtech user might have valuable data, such as secret grow recipes for unique strains of plants, user ID, anonymity of the user or data on revenue-based ag-tech businesses.
The database includes the following details: the area planted; the species planted; percentage seedlings survival; number of community and private forests and related data such as total area, growing stock and location.
