General Mills Optimized Stratified Sampling
“Soil Organic Carbon (SOC) sampling is a crucial yet costly procedure with immense applications in the field of environmental and agronomic research, management, and policy.”
An API Powered by Trailblazing Data Sources
We utilized state-of-the-art geospatial data sets obtained from NASA satellites such as LANDSAT-9 and SENTINEL-2, powered by a combination of Google Earth Engine (geemap) as a planetary-scale software for Earth science and data analysis.
In addition, we capitalized on various variables extracted from Gridded Soil Survey Geographic (gSSURGO), a high-definition national geodatabase of the United States of America. We then carefully pruned 82 features to most relevant to our cause.
Pioneering Machine Learning Modelling
We designed and implemented a mathematically optimized Bayesian stratification and sampling algorithm, derived from unsupervised clustering techniques united with cutting-edge Conditioned Latin Hypercube Sampling (cLHS) methodology.
Harnessing the power of an advanced tree-based machine learning model, computations are rolled out and served in form of a scalable, generalizable and interpretable estimation system producing industry-standard insights (70% CI; 10% ME).