Quantifying Error in Training Data for Mapping and Monitoring the Earth System - A Workshop on “Quantifying Error in Training Data for Mapping and Monitoring the Earth System” was held on January 8-9, 2019 at Clark University, with support from Omidyar Network’s Property Rights Initiative, now PlaceFund.

1. 8
Circle Bias, many
false positive
identified because
of overreliance on
circular features
https://github.com/ecoh
ydro/CropMask_RCNN

2. Probability
score above
.7 deemed a
center pivot
Tested on
never before
seen
512x512 tiles
11
Some center
pivots are
missed
because of
date mismatch
between
imagery and
labels of the
reference
dataset