Forest-based conformal prediction approach for spatial data

Modeling Spatial Extremes Using Non‐Gaussian Spatial Autoregressive Models via Convolutional Neural Networks

A spatial autoregressive modeling framework, which maps observations at a location and its neighbors to independent random variables, and applies this model to analyze annual maximum precipitation data from ERA‐Interim‐driven Weather Research and Forecasting simulations, allowing it to explore its spatial extreme behav...

GeoDose-CP: Graph-Local Conformal Inference for Continuous-Treatment Earth Observation

Reliable intervention-oriented uncertainty quantification from Earth observation (EO) remains challenging when continuous treatment shifts, spatial dependence, limited support, and satellite-outcome uncertainty must be addressed simultaneously. Existing causal, conformal, and spatial approaches address parts of this pr...

Novel analogue-based and geostatistical approaches for space-time prediction of environmental variables

A Graph-Based Solution for RFM Problem via Accelerated ADMM

The rational function model (RFM) is crucial for the geometric processing of remote sensing satellite imagery. However, estimating the unknown rational polynomial coefficients (RPCs) remains an ill-posed and over-parameterized problem due to severe multicollinearity. Existing regularization methods predominantly rely o...

Beyond Point Forecasts: A Survey on Probabilistic Forecasting for Time Series and Spatiotemporal Data

This survey develops a unified perspective by organizing probabilistic forecasting methods according to where and how uncertainty is introduced into the forecasting pipeline, and identifies unresolved challenges surrounding uncertainty in evolving dependency structures, physics-informed predictive distributions, foreca...

Positional Encoding Enhanced Graph Quantile Learning for Uncertainty-Aware Spatial Prediction

This work introduces the Graph Quantile Neural Network (GQNN) with Positional Encoding (PE), whichintegrates graph-based spatial representation learning, the sinusoidal positional encoding, and ordered multi-quantile regression.