Combining kernel density and colocation statistics with ResNet50 raises test accuracy on terrain feature classification from 0.683 to 0.718.
Sphere2Vec: A general-purpose location representation learning over a spherical surface for large -scale geospatial predictions,
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Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification
Combining kernel density and colocation statistics with ResNet50 raises test accuracy on terrain feature classification from 0.683 to 0.718.