arXiv Machine Learning
Jul 24
STeMP: Spatio-Temporal Modelling Protocol
arXiv:2607. 20592v1 Announce Type: new Abstract: Spatio-temporal machine-learning modelling is an important tool in environmental research.
STeMP: Spatio-Temporal Modelling Protocol
Spatio-temporal machine-learning modelling is an important tool in environmental research. However, machine-learning models are highly sensitive to both the characteristics of the training data, such as its distribution, and methodological choices, including the cross-validation strategy.
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arXiv Machine Learning
Jul 2
OpFML: Pipeline for ML-based Operational Inference
arXiv:2601. 11046v2 Announce Type: replace Abstract: Machine learning models for climate and Earth science are becoming increasingly capable, yet model deployment into operational use remains a largely unaddressed challenge: general-purpose model-serving tools, such as MLflow and KServe, assume input data availability at the inference node, while data acquisition, failure handling, and preprocessing are trusted to a separate workflow.
arXiv Machine Learning
Jul 2
Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling
arXiv:2607. 01022v1 Announce Type: new Abstract: Spatiotemporal point processes (STPPs) model event data in continuous time and space, with applications in mobility, epidemiology, and public safety.
arXiv AI
Jun 9
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
arXiv:2310. 10196v3 Announce Type: replace-cross Abstract: Temporal data, including time series and spatio-temporal data, are pervasive in real-world applications.
arXiv AI
Jul 28
Every Client Is an Environment: Federated De-confounding for Spatio-Temporal Forecasting
arXiv:2607. 24218v1 Announce Type: cross Abstract: Federated learning has emerged as a promising paradigm for spatio-temporal forecasting (STF), enabling collaborative model training without sharing raw observations.
arXiv Machine Learning
Jun 5
CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting
arXiv:2606. 05413v1 Announce Type: new Abstract: As urban environments continue to evolve rapidly, accurately modeling the dynamic behaviour of Points of Interest is essential for supporting data-driven urban planning and commercial decision-making.
Hugging Face Trending Papers
Aug 3
GISAgentBench: A Practitioner-Sourced Benchmark for Evaluating LLM Agents on GIS Tasks
Geographic Information System (GIS) professionals rely on multi-step spatial analysis workflows to support decision-making in urban planning, disaster response, and environmental monitoring. The process is tedious, time-consuming, and error-prone.
arXiv Machine Learning
Jul 28
HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows
arXiv:2607. 23983v1 Announce Type: cross Abstract: Operational flood forecasting depends on tacit forecaster expertise that is difficult to formalize, audit, and transfer.
arXiv AI
Jun 4
Physics-Informed Machine Learning for Short-Term Flood Prediction
arXiv:2606. 04143v1 Announce Type: cross Abstract: Accurate flood forecasting is essential for mitigating disaster risks and protecting communities.
arXiv AI
Jul 7
A harmonised dataset for Earth system foundation models
arXiv:2607. 03298v1 Announce Type: cross Abstract: Foundation models for Earth systems have so far been trained primarily on physical climate and weather data, with limited representation of the human systems that both drive and respond to environmental change.
arXiv AI
Jul 21
An Agentic Interface for End-to-End Probabilistic Seismic Hazard and Risk Analysis
arXiv:2607. 16249v1 Announce Type: cross Abstract: Probabilistic seismic hazard and risk analyses are backbone to building codes, insurance pricing, and disaster management.
arXiv AI
Aug 6
CheMLFlow: An Open-Source Platform for Cheminformatics and Materials Informatics Applications
arXiv:2608. 04942v1 Announce Type: cross Abstract: CheMLFlow is an open-source platform for building and executing end-to-end, high-throughput, and agentic workflows for scientific and technological applications.