A transformer-based in-context learning model predicts continental-scale subsurface temperatures from sparse borehole observations, outperforming physics and interpolation baselines while adapting to new regions with 20 examples.
A computer movie simulating urban growth in the detroit region.Economic geography, 46(sup1):234–240
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NARA introduces a unified self-supervised method for learning relational, context-dependent representations of heterogeneous vector geoentities that improves performance on building classification, traffic prediction, and POI recommendation.
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In-context learning enables continental-scale subsurface temperature prediction from sparse local observations
A transformer-based in-context learning model predicts continental-scale subsurface temperatures from sparse borehole observations, outperforming physics and interpolation baselines while adapting to new regions with 20 examples.
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NARA: Anchor-Conditioned Relation-Aware Contextualization of Heterogeneous Geoentities
NARA introduces a unified self-supervised method for learning relational, context-dependent representations of heterogeneous vector geoentities that improves performance on building classification, traffic prediction, and POI recommendation.