Multi-Modal Spatio-Temporal Graph Neural Network with Mixture of Experts for Soil Organic Carbon Prediction

• URL: http://arxiv.org/abs/2606.16580v1
• Date: Mon, 15 Jun 2026 11:25:38 GMT
• Title: Multi-Modal Spatio-Temporal Graph Neural Network with Mixture of Experts for Soil Organic Carbon Prediction
• Abstract summary: Existing approaches face two limitations: they pair hand-crafted co-modals with classical ML or single-modal deep models that miss rich spectral and temporal information.<n>We introduce SpTG-NN, a multi-temporal graph neural network addressing both SpTGNN and grid-based architectures.<n>A fine-tuned TerraMind encoder extracts node features from Sentinel-2, Sentinel-1 and DEM signals.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Top-soil organic carbon (SOC) prediction is fundamental to agricultural sustainability, land use policy and fertilization planning. Existing approaches face two limitations: they pair hand-crafted covariates with classical ML or single-modal deep models that miss rich spectral and temporal information, and grid-based architectures ignore the irregular spatial structure of field measurements. We introduce SpTGNN, a multi-modal spatio-temporal graph neural network addressing both. SpTGNN represents soil measurements as nodes in a heterogeneous graph with three edge types (spatial proximity, spectral similarity, elevation), and applies relational graph attention to learn separate patterns per relation. A fine-tuned TerraMind encoder extracts node features from Sentinel-2, Sentinel-1 and DEM signals, combined with per-sample environmental covariates and learned positional and temporal embeddings. A sparse Mixture-of-Experts module fuses the four streams via top-$k$ routing. Uncertainty is captured by pairing heteroscedastic regression (aleatoric) with deep ensembles (epistemic), and a Moran's $I$ penalty regularizes spatial autocorrelation. We evaluate on a global SOC corpus split into three regional instances ($\sim$49k samples globally, Africa $\sim$26k, Europe $\sim$14k). Our 5-member deep ensemble reports $R^2=0.762$, RMSE $=3.51\pm0.48$ g/kg and MAPE $=22.9\%$ on the Africa test split, improving over a tabular XGBoost baseline; the best single checkpoint reaches validation $R^2=0.864$. Ablations confirm the heterogeneous graph, MoE fusion and fine-tuned backbone each contribute substantively, and the ensemble UQ stack achieves post-calibration ECE of $0.031$ (hybrid) and $0.026$ ($β$-NLL). To our knowledge, this is the first framework to unify foundation-model feature extraction, heterogeneous graph attention and decomposed uncertainty quantification for SOC estimation.

Related papers

• Structural Kolmogorov-Arnold Convolutions: Learnable Function on the Values or the Filter Shape as Parameter-Efficient Alternative to Per-Edge Convolutional KANs [76.06235645266621]
  Convolutional Kolmogorov--Arnold Networks (KANs) replace the fixed weights of a convolutional kernel with learnable univariate functions.<n>We argue that the learnable functions are better placed in the emphstructure of the convolution than on each edge.
  arXiv  Detail & Related papers  (2026-06-23T10:00:26Z)
• $p$-adic Bi-Filtrations for Topological Machine Learning on Genomic Sequences [1.0165640083594571]
  pVR is a machine learning framework for alignment-free genomic sequence classification.<n>It combines $p$-adic numbers with topological data analysis.
  arXiv  Detail & Related papers  (2026-06-04T13:05:36Z)
• Three Costs of Amortizing Gaussian Process Inference with Neural Processes [0.0]
  Neural processes amortize Gaussian process inference, replacing the exact $O(n3)$ posterior with a learned $O(n)$ map from context sets to predictive distributions.<n>For a class of latent neural processes, we bound the Kullback--Leibler (KL) divergence between the GP and LNPs, decomposing it into three interpretable sources.<n>These results characterize the costs of amortization within the analyzed class and yield architectural recommendations to predict variance from context locations alone in the GP-amortization regime.
  arXiv  Detail & Related papers  (2026-05-20T22:48:23Z)
• A Closed-Form Adaptive-Landmark Kernel for Certified Point-Cloud and Graph Classification [0.0]
  PALACE is the strongest closed-form diagram-based method on OrbitHS5k ($91.3pm 1.0%$, matching Persformer)<n>At $8$ domain inflation, placement maintains $94%$ while the uniform grid collapses to chance ($25%$ on 4-class data)<n> Empirically, PALACE is the strongest closed-form diagram-based method on OrbitHS5k ($91.3pm 1.0%$, matching Persformer)
  arXiv  Detail & Related papers  (2026-05-05T17:59:18Z)
• Minimax Optimality and Spectral Routing for Majority-Vote Ensembles under Markov Dependence [6.908972852063454]
  Majority-vote ensembles achieve variance reduction by averaging over diverse, approximately independent base learners.<n>We provide a minimax characterization of this phenomenon for discrete classification in a fixed-dimensional Markov setting.<n>Experiments on synthetic Markov chains, 2D spatial grids, the 128-dataset UCR archive, and Atari DQN ensembles validate the theoretical predictions.
  arXiv  Detail & Related papers  (2026-04-15T02:32:30Z)
• SA-CycleGAN-2.5D: Self-Attention CycleGAN with Tri-Planar Context for Multi-Site MRI Harmonization [3.5109108807229403]
  We propose SA-CycleGAN-2.5D, a domain adaptation framework motivated by the $HH$-divergence bound of Ben-David et al.<n>By bridging 2D efficiency and 3D consistency, our framework yields voxel-level harmonized images that preserve tumor pathophysiology.
  arXiv  Detail & Related papers  (2026-03-17T23:49:46Z)
• Near-Optimal Clustering in Mixture of Markov Chains [74.3828414695655]
  We study the problem of clustering $T$ trajectories of length $H$, each generated by one of $K$ unknown ergodic Markov chains over a finite state space of size $S$.<n>We derive an instance-dependent, high-probability lower bound on the clustering error rate, governed by the weighted KL divergence between the transition kernels of the chains.<n>We then present a novel two-stage clustering algorithm.
  arXiv  Detail & Related papers  (2025-06-02T05:10:40Z)
• Collaborative non-parametric two-sample testing [55.98760097296213]
  The goal is to identify nodes where the null hypothesis $p_v = q_v$ should be rejected. We propose the non-parametric collaborative two-sample testing (CTST) framework that efficiently leverages the graph structure. Our methodology integrates elements from f-divergence estimation, Kernel Methods, and Multitask Learning.
  arXiv  Detail & Related papers  (2024-02-08T14:43:56Z)
• Information-Theoretic Thresholds for Planted Dense Cycles [52.076657911275525]
  We study a random graph model for small-world networks which are ubiquitous in social and biological sciences. For both detection and recovery of the planted dense cycle, we characterize the information-theoretic thresholds in terms of $n$, $tau$, and an edge-wise signal-to-noise ratio $lambda$.
  arXiv  Detail & Related papers  (2024-02-01T03:39:01Z)
• Detection-Recovery Gap for Planted Dense Cycles [72.4451045270967]
  We consider a model where a dense cycle with expected bandwidth $n tau$ and edge density $p$ is planted in an ErdHos-R'enyi graph $G(n,q)$. We characterize the computational thresholds for the associated detection and recovery problems for the class of low-degree algorithms.
  arXiv  Detail & Related papers  (2023-02-13T22:51:07Z)
• $\ exttt{FedBC}$: Calibrating Global and Local Models via Federated Learning Beyond Consensus [66.62731854746856]
  In federated learning (FL), the objective of collaboratively learning a global model through aggregation of model updates across devices tends to oppose the goal of personalization via local information. In this work, we calibrate this tradeoff in a quantitative manner through a multi-criterion-based optimization. We demonstrate that $texttFedBC$ balances the global and local model test accuracy metrics across a suite datasets.
  arXiv  Detail & Related papers  (2022-06-22T02:42:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.