Updated Oct 06, 2020
Spatial data outlier detection and MODIS product version comparison applications for Google Earth Engine
Part of Michigan State University
http://www.ksu.edu/siil
Joseph Messina
Open access spatial data outlier detection and product version comparison applications for Google Earth Engine that allows modelers and other users to minimize error and uncertainty in remote sensing data models.
Stage 5: Scaling
Focus Areas:
Agriculture
Implemented In:
Malawi and United States
2
Countries Implemented In
Verified Funding
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Problem
Access to accurate digital data to reduce error and uncertainty in geospatial models.
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Solution
This tool reduces error and uncertainty 100% of the time if used properly and is completely publicly accessible. It also helps other models to be more accurate in reducing errors and uncertainty in both inputs and resulting estimates.
Target Beneficiaries
Primary beneficiaries are modelers and others doing agriculture policy work at national and international scales. Secondary beneficiaries are those interested in using digital tools for predictions.
Competitive Advantage
A tool such as this has not been created. The tool is a great benefit to the field for modelers and others working in agricultural reform.
Milestone
Date Unknown
Funds RaisedVERIFIED
TITLEPrecision Agriculture for Smallholder Systems in Africa
TYPEGrant
FOCUS AREAS
Agriculture
Implemented InCambodia, Bangladesh, United States and 1 more SEE ALLCambodia, Bangladesh, United States and Malawi SEE LESS
ORGANIZATIONSU.S. Agency for International Development, Kansas State UniversityU.S. Agency for International Development, Kansas State University
as part of
Bureau of Food Security, Feed the Future, Feed the Future Innovation Lab for Collaborative Research on Sustainable IntensificationBureau of Food Security, Feed the Future, Feed the Future Innovation Lab for Collaborative Research on Sustainable IntensificationDate Unknown
New Country Implemented In
United States
Date Unknown
New Country Implemented In
Malawi
