Quantifying uncertainty in area and regression coefficient estimation from remote sensing maps

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.