Crop Mapping Evaluation under Sampling Bias
Government agencies use crop maps to estimate production, monitor food security, and set agricultural policy. These crop maps are usually validated against field labels, but these labels are rarely a random sample of the area the map covers. Field teams tend to label parcels near roads and skip ones that are small or hard to reach, so performance measured on held-out labels mainly describes the places they visited. We call the difference between this reported performance and performance over the whole mapped region the deployment performance gap. To estimate performance over the whole region, we use a weighted form of prediction-powered inference (PPI). The classifier's predictions on unlabeled points across the region give a first estimate, and its errors on the labeled points correct that estimate, with more weight on labels from under-sampled conditions. We test this in five European regions where crop registers label every declared parcel, so the true performance is known. Simulating six documented collection practices there produces gaps of up to 0.20 macro-F1. On the collection rule that creates the largest gap in each region, 5 km block cross-validation still overestimates macro-F1 by as much as 0.18, while PPI with weights estimated from satellite embeddings reduces the largest error across regions to 0.07. Importance-weighted cross-validation with the same weights does almost as well, so most of the improvement comes from the weights themselves. We also apply the method to three Kenyan datasets that have no region-wide reference labels. For a roadside dataset collected with helmet-mounted cameras, the estimated macro-F1 falls from 0.93 under random cross-validation to 0.61, although the correction relies on a small number of heavily weighted labels. Our code is available at microsoft/pappi.
Comments: 17 pages, 4 figures, 5 tables. Code: microsoft/PaPPI
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Crop Mapping Evaluation under Sampling Bias Government agencies use crop maps to estimate production, monitor food security, and set agricultural policy. These crop maps are usually validated against field labels, but these labels are rarely a random sample of the area the map covers. Field teams tend to label parcels near roads and skip ones that are small or hard to reach, so performance measured on held-out labels mainly describes the places they visited. We call the difference between this reported performance and performance over the whole mapped region the deployment performance gap. To estimate performance over the whole region, we use a weighted form of prediction-powered inference (PPI). The classifier's predictions on unlabeled points across the region give a first estimate, and its errors on the labeled points correct that estimate, with more weight on labels from under-sampled conditions. We test this in five European regions where crop registers label every declared parcel, so the true performance is known. Simulating six documented collection practices there produces gaps of up to 0.20 macro-F1. On the collection rule that creates the largest gap in each region, 5 km block cross-validation still overestimates macro-F1 by as much as 0.18, while PPI with weights estimated from satellite embeddings reduces the largest error across regions to 0.07. Importance-weighted cross-validation with the same weights does almost as well, so most of the improvement comes from the weights themselves. We also apply the method to three Kenyan datasets that have no region-wide reference labels. For a roadside dataset collected with helmet-mounted cameras, the estimated macro-F1 falls from 0.93 under random cross-validation to 0.61, although the correction relies on a small number of heavily weighted labels. Our code is available at microsoft/pappi. Comments: 17 pages, 4 figures, 5 tables. Code: microsoft/PaPPI