Spatial Interpolation

Spatial interpolation aims to estimate values at unsampled locations based on known values at nearby points, a crucial task across diverse scientific fields. Current research emphasizes developing sophisticated models that go beyond traditional methods like Kriging and Inverse Distance Weighting by incorporating machine learning techniques such as neural networks (including those with attention mechanisms), ensemble learning, and self-supervised learning approaches. These advancements improve accuracy, particularly in handling complex, non-linear spatial relationships and quantifying uncertainty, leading to more reliable predictions in applications ranging from reservoir modeling and house price estimation to weather forecasting and traffic flow analysis.