arXiv Machine Learning

Predicting Groundwater Arsenic Concentrations Using Graph Neural Networks

arXiv:2607. 19392v1 Announce Type: new Abstract: Arsenic contamination in groundwater presents a longstanding public health crisis in the United States, especially for households depending on private wells.

arXiv Machine Learning

Jun 18

UST-GNN: A Unified Spatial--Topological Graph Neural Network Framework for Urban Analytics--Demonstrated through a Case Study on Urban Health Prediction

arXiv:2504. 04739v3 Announce Type: replace Abstract: Understanding how social, demographic, environmental, and spatial factors jointly shape urban outcomes is essential for sustainable urban development and evidence-based policy.

By Minwei Zhao, Sanja Scepanovic, Stephen Law, Ivica Obadic, Cai Wu, Daniele Quercia

rag safety

arXiv AI

Jul 28

Spatial Prediction of Soil Microplastics and Organic Matter Using Graph Attention Networks

arXiv:2607. 22875v1 Announce Type: cross Abstract: Accurate estimation of soil microplastics and organic matter is essential to assess ecosystem health and support sustainable land use.

By Anik Dev Nath, Md Al Amin, Bikash Kumar Paul

arXiv Machine Learning

Jun 25

Auto-Configured Explainable Graph Neural Networks for Multi-Site Pollution Prediction

arXiv:2606. 24978v1 Announce Type: new Abstract: Accurate particulate matter (PM) prediction is crucial for mitigating air pollution.

By Abdelkader Dairi, Fouzi Harrou, Ying Sun

arXiv Machine Learning

Jun 16

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

arXiv:2606. 16580v1 Announce Type: new Abstract: Top-soil organic carbon (SOC) prediction is fundamental to agricultural sustainability, land use policy and fertilization planning.

By Daniele Mos, Felipe Drummond, Anton Bossenbroek, Soufiane el Khinifri

rag fine-tuning multimodal

arXiv AI

Jul 3

Spatial Support Matters: Geometry-Aware Graph Fusion for Rainfall Field Reconstruction

arXiv:2607. 01621v1 Announce Type: new Abstract: Fine-scale rainfall reconstruction is critical for urban flood modeling, but real rainfall sensing systems observe the field through incompatible spatial supports: gauges measure points, microwave links measure paths, and radar/satellite products measure gridded areas.

By Low Jun Yu, Niramay Kachhadiya, Herath Mudiyanselage Viraj Vidura Herath, Sanka Rasnayaka, Lucy Amanda Marshall

rag

arXiv Machine Learning

Jun 24

Reconstructing GRACE Terrestrial Water Storage with Spatio-Temporal Graph Neural Networks: An Application to South America

arXiv:2606. 23833v1 Announce Type: new Abstract: Terrestrial water storage (TWS) integrates snow, soil moisture, surface water, and groundwater and is a key indicator of how climate variability and human activity reshape the global water cycle.

By Lukas Arzoumanidis, Lara Johannsen, Klara Middendorf, Annette Eicker, Youness Dehbi

safety

arXiv AI

Jun 11

FOCUS on Contamination: Hydrology-Informed Noise-Aware Learning for Geospatial PFAS Mapping

arXiv:2502. 14894v5 Announce Type: replace-cross Abstract: Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with significant public health impacts, yet large-scale monitoring remains severely limited due to the high cost and logistical challenges of field sampling.

By Jowaria Khan, Alexa Friedman, Sydney Evans, Rachel Klein, Runzi Wang, Katherine E. Manz, Kaley Beins, David Q. Andrews, Elizabeth Bondi-Kelly

computer-vision

arXiv AI

Jun 9

OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs

arXiv:2606. 08046v1 Announce Type: new Abstract: We present OSMGraphCLIP, a CLIP-style geospatial representation model that learns global location embeddings from freely available OpenStreetMap (OSM) data.

By Dimitrios Michail, Eleni Saka, Ioannis Giannopoulos, Ioannis Papoutsis

rag benchmarks safety

arXiv AI

Jun 9

SNR-ST-Mix: Sample-specific Neighborhood Regression Mixup for Augmented Spatial Transcriptomics Imputation with Deep Neural Network

arXiv:2606. 08712v1 Announce Type: cross Abstract: Purpose: Spatial transcriptomics (ST) enables gene expression measurements within the tissue context.

By Hongyi Yu, Yaoyu Fang, Jiahe Qian, Xinkun Wang, Lee A. Cooper, Bo Zhou

arXiv Machine Learning

Jun 2

AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks

arXiv:2606. 01283v1 Announce Type: new Abstract: Modeling spatial dependencies is central to spatiotemporal data analysis using Graph Neural Networks (GNNs).

By Zhongyue Zhang, Guangyin Jin, Yuxuan Liang, Suwan Yin, Yuankai Wu

arXiv Machine Learning

Jul 20

DELUGE: Towards Continental-Scale Daily Pluvial Flood Damage Prediction via Interpretable Conditioning on Foundation Model Embeddings

arXiv:2607. 16050v1 Announce Type: new Abstract: Pluvial (rainfall-driven) flooding accounts for 45% of National Flood Insurance Program (NFIP) claims in the United States and is harder to predict than its riverine and coastal counterparts, with existing approaches limited to coarse resolution, regional domains, or computationally intensive process-based models unsuitable for daily continental-scale use.

By Yuya Kawakami, Daniel Cayan, Dongyu Liu, Kwan-Liu Ma, Tom Corringham

rag multimodal safety

arXiv Machine Learning

Jun 19

Towards Graph-Based Deep Learning for Map Generalization: Insights from Building Footprints Simplification and Aggregation

arXiv:2606. 19956v1 Announce Type: new Abstract: Map generalization remains one of the fundamental tasks in cartography, especially for the simplification and aggregation of complex building footprints.

By Yanning Wang, Zhiyong Zhou, Zhouyu Liu, Mengni Yu, Yu Feng

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