New AI framework models power-line risk using remote sensing data

A new probability-of-failure (PoF) modeling framework has been developed for electric power grid asset management, integrating remote sensing data to predict risks from lightning and vegetation. This modular and explainable system is designed for scalability and operational maintainability, allowing adaptation to new data sources and failure modes. The framework utilizes a harmonized geospatial machine-learning pipeline with predictors such as topography, vegetation condition, lightning climatology, and proximity features to provide actionable asset-level risk stratification for improved network resilience and operational planning. AI

IMPACT This framework could enhance the reliability and resilience of critical infrastructure by enabling more precise risk assessment and proactive maintenance.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new machine learning framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Artur Sokolovsky
• arXiv
• LIS VHRMC
• Moderate-Resolution Imaging Spectroradiometer
• Normalized Difference Vegetation Index
• OpenStreetMap
• Shuttle Radar Topography Mission

• paper
• product
• infra

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