Puffin: Probabilistic Learning of Spatial Detail From Coarse Observations
We introduce Puffin, a probabilistic framework for statistical disaggregation that raises the resolution of coarse totals using high-resolution satellite embeddings as covariates.
ProofPaper ↗
Key points
- High-resolution socioeconomic variables are important for applications such as urban planning, public health, disaster response, and resource allocation.
- In practice, however, these variables are often observed only at a coarse spatial resolution.
- Instead of predicting a single value for each fine-resolution subregion, Puffin learns a probability distribution and is trained through an aggregation-aware likelihood.
- We evaluate Puffin on German and US census, employment, and election data across population, jobs, and other count variables, and study when statistical disaggregation succeeds or fails across regions, countries, and targets.
Sources (1)
- [1]Puffin: Probabilistic Learning of Spatial Detail From Coarse ObservationsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 01:12 PM
We introduce Puffin, a probabilistic framework for statistical disaggregation that raises the resolution of coarse totals using high-resolution satellite embeddings as covariates.
High-resolution socioeconomic variables are important for applications such as urban planning, public health, disaster response, and resource allocation.
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