Sherrie Wang: Reliable conclusions from remote sensing maps

Remote sensing maps are used to estimate regression coefficients relating environmental variables, such as 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. 

Thus, population-level estimates from such maps may be biased. We show how a small amount of randomly sampled ground truth data can correct for bias in large-scale remote sensing map products. Applying our method across multiple remote sensing use cases in regression coefficient estimation, we find that it results in estimates that are:

 (1) more reliable than using the map product as if it were 100% accurate and 

(2) have lower uncertainty than using only the ground truth and ignoring the map product.

Paper: Regression coefficient estimation from remote sensing maps

Sherrie Wang’s research uses novel data and computational algorithms to monitor our planet and enable sustainable development. Her focus is on improving agricultural management and mitigating climate change, especially in low- or middle-income regions of the world. To this end, she frequently uses satellite imagery, crowdsourced data, LiDAR, and other spatial data. Due to the scarcity of ground truth data in these regions and the noisiness of real-world data in general, her methodological work is geared toward developing machine learning methods that work well with these constraints.

Prior to MIT, Wang was a Ciriacy-Wantrup Postdoctoral Fellow at UC Berkeley, hosted by Solomon Hsiang and the Global Policy Lab. In 2021, she obtained her PhD in Computational and Mathematical Engineering from Stanford University, where she was advised by David Lobell and benefited from mentors at the Center on Food Security and the Environment and the Sustainability and AI Lab.