GAIR introduces a geo-aligned implicit representation module inside a multi-encoder contrastive SSL framework that produces location-aware embeddings and outperforms prior geo-foundation models on 22 geospatial datasets across 9 tasks.
Torchspatial: A location encoding framework and benchmark for spatial representation learning
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Benchmark finds location encoders recover primary spatial coefficients consistently but secondary ones vary by scale, with raw-coordinate baseline competitive throughout.
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GAIR: Location-Aware Self-Supervised Contrastive Pre-Training with Geo-Aligned Implicit Representations
GAIR introduces a geo-aligned implicit representation module inside a multi-encoder contrastive SSL framework that produces location-aware embeddings and outperforms prior geo-foundation models on 22 geospatial datasets across 9 tasks.
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Do Location Encoders Capture Spatial Effects? A GeoShapley Benchmark Across Scales
Benchmark finds location encoders recover primary spatial coefficients consistently but secondary ones vary by scale, with raw-coordinate baseline competitive throughout.