  Remote sensing map products are used to obtain estimates of environmental
quantities, such as deforested area or the effect of conservation zones on
deforestation. However, the quality of map products varies, and - because maps
are outputs of complex machine learning algorithms that take in a variety of
remotely sensed variables as inputs - errors are difficult to characterize.
Without capturing the biases that may be present, naive calculations of
population-level estimates from such maps are statistically invalid. In this
paper, we compare several uncertainty quantification methods - stratification,
Olofsson area estimation method, and prediction-powered inference - that
combine a small amount of randomly sampled ground truth data with large-scale
remote sensing map products to generate statistically valid estimates. Applying
these methods across four remote sensing use cases in area and regression
coefficient estimation, we find that they result in estimates that are more
reliable than naively using the map product as if it were 100% accurate and
have lower uncertainty than using only the ground truth and ignoring the map
product. Prediction-powered inference uses ground truth data to correct for
bias in the map product estimate and (unlike stratification) does not require
us to choose a map product before sampling. This is the first work to (1) apply
prediction-powered inference to remote sensing estimation tasks, and (2)
perform uncertainty quantification on remote sensing regression coefficients
without assumptions on the structure of map product errors. To improve the
utility of machine learning-generated remote sensing maps for downstream
applications, we recommend that map producers provide a holdout ground truth
dataset to be used for calibration in uncertainty quantification alongside
their maps.
