Creating Training Dataset and ML Model to Detect Field Boundaries from Satellite Imagery
Without proper collection, curation, and preparation, data collected on the ground can often lack the details and standards required for use in ML modeling, resulting in a gap in applications, particularly in agricultural monitoring. Radiant Earth Foundation and IDinsight will collect data on crop types and field boundaries across five states in India to empower practitioners and stakeholders across developing regions to use machine learning (ML) and earth observation (EO) for agricultural decision-making at both field and national levels. The team will collect ground reference data to generate a training dataset and develop a baseline ML model. The open-access high-quality ground reference data, benchmark training datasets, and models will enable the ecosystem to facilitate the standardization of data collection and models that benefit decision-makers and farmers.
Hamed Alemohammad, Radiant Earth Foundation
Amber Myers, Radiant Earth Foundation
Andrew Fraker, IDinsight
Ben Brockman, IDinsight