Scalable Matching of Agricultural Field Polygons for Large-Scale Data Comparisons

Advances in deep learning and data harmonization led to a growing availability of large datasets of agricultural field polygons of different granularity and quality. Identifying correspondences between datasets covering the same geographic regions enables opportunities for ● Data Quality Assessment: validation of model outputs against reference● Data Fusion & Integration: combine complementary information● Temporal Change Analysis: monitoring of what has changed over time