Xingu: Explaining critical geospatial predictions in weak supervision for climate finance (Proposals Track)
David Dao (ETH Zurich); Johannes Rausch (ETH Zurich); Ce Zhang (ETH); Iveta Rott (ETH Zurich)
Monitoring, Reporting, and Verification (MRV) play a crucial key role in the decision-making of climate investors, policymakers and conservationists. Remote sensing is commonly used for MRV but practical solutions are constrained by a lack of labels to train machine learning-based downstream tasks. Recent work leverages weak supervision to alleviate the problem of labelled data scarcity. However, the definition of weak supervision signals is limited by the existence of millions of possible heuristic-based feature generation rules. Furthermore, these rules are often difficult to interpret for climate finance and underperform in critical data subsets. We propose Xingu, an interpretable MRV system to explain weak supervision rules using game-theoretic SHAP values for critical model predictions. Moreover, Xingu enables domain experts to collectively design and share labelling functions, thus curating a reusable knowledge base for weak supervision signals.