Mitigating Bad Ground Truth in Supervised Machine Learning based Crop Classification: A Multi-Level Framework with Sentinel-2 Images
Journal:
arXiv
Published Date:
Mar 14, 2025
Abstract
In agricultural management, precise Ground Truth (GT) data is crucial for
accurate Machine Learning (ML) based crop classification. Yet, issues like crop
mislabeling and incorrect land identification are common. We propose a
multi-level GT cleaning framework while utilizing multi-temporal Sentinel-2
data to address these issues. Specifically, this framework utilizes generating
embeddings for farmland, clustering similar crop profiles, and identification
of outliers indicating GT errors. We validated clusters with False Colour
Composite (FCC) checks and used distance-based metrics to scale and automate
this verification process. The importance of cleaning the GT data became
apparent when the models were trained on the clean and unclean data. For
instance, when we trained a Random Forest model with the clean GT data, we
achieved upto 70\% absolute percentage points higher for the F1 score metric.
This approach advances crop classification methodologies, with potential for
applications towards improving loan underwriting and agricultural
decision-making.
